{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "
\n", "\"FMP\"\n", "\"AudioLabs\"\n", "
" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "
\n", "\"C5\"\n", "

Template-Based Chord Recognition

\n", "
\n", "\n", "
\n", "\n", "

\n", "Following Section 5.2 of [Müller, FMP, Springer 2015], we introduce in this notebook a basic approach for chord recognition using chord templates. For a discussion of the importance of various algorithmic components of such a system, we refer to the following two studies:\n", "\n", "

\n", "

" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Introduction\n", "\n", "In music, **harmony** refers to the simultaneous sound of different notes that form a cohesive entity in the mind of the listener. The main constituent components of harmony, at least in the Western music tradition, are [**chords**](../C5/C5S1_Chords.html), which are musical constructs\n", "that typically consist of three or more notes. Harmony analysis may be thought of as the study of the construction, interaction, and progression of chords. In this notebook, we discuss a subproblem of harmonic analysis referred to as **chord recognition**, where we consider only a small number of the most important chords as occurring in Western music. Furthermore, we assume that the piece of music is given in the form of an **audio recording**. The resulting chord recognition task consists in splitting up the recording into **segments** and assigning a **chord label** to each segment. The segmentation specifies the start and end time of a chord, and the chord label specifies which chord is played during this time period. A typical chord recognition system consists of **two main steps**. \n", "\n", "* In the first step, the given audio recording is cut into frames, and each frame is transformed into an appropriate **feature vector**. Most recognition systems rely on [**chroma-based audio features**](../C3/C3S1_SpecLogFreq-Chromagram.html), which correlate to the underlying tonal information contained in the audio signal. \n", "* In the second step, **pattern matching** techniques are used to map each feature vector to a set of predefined **chord models**. The best fit determines the chord label assigned to the given frame. \n", "\n", "To improve the chord recognition results, additional enhancement techniques are applied either before the pattern matching step (referred to as [**prefiltering**](../C5/C5S3_ChordRec_HMM.html)) or after/within the pattern matching step \n", "(referred to as [**postfiltering**](../C5/C5S3_ChordRec_HMM.html)). In this notebook, we introduce a first chord recognition procedure that employs a simple template-based matching strategy.\n", "\n", "\"FMP_C5_F13\"" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Beatles Example\n", "\n", "In the following example, the chord recognition task is illustrated by the first measures of the Beatles song \"Let It Be.\" The figure shows a score representation, the recording's waveform, a [chromagram](../C3/C3S1_SpecLogFreq-Chromagram.html), as well as chord annotations generated in a manual process. \n", "\n", "\"FMP_C5_F01\"\n", "\n", "
\n", "\n", "" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Chroma-Based Feature Representation\n", "\n", "Given an audio recording of a piece of music, the first step is to transform the recording into a sequence $X=(x_1,x_2,\\ldots,x_N)$ of feature vectors $x_n\\in\\mathcal{F}$, $n\\in[1:N]$, where $\\mathcal{F}$ denotes a suitable feature space. As mentioned above, nearly all traditional chord recognition procedures rely on some type of [**chroma-based feature representation**](../C3/C3S1_SpecLogFreq-Chromagram.html). This is because chroma-based features capture a signal's short-time tonal content, which is closely correlated to the harmonic progression of the underlying piece. Assuming the [equal-tempered scale](../C1/C1S1_MusicalNotesPitches.html), the chroma values correspond to the set $\\{\\mathrm{C},\\mathrm{C}^\\sharp,\\mathrm{D},\\ldots,\\mathrm{B}\\}$, which we identify with the set $[0:11]$. A chroma feature can then be expressed as a $12$-dimensional vector \n", "\n", "$$\n", "x=(x(0),x(1),\\ldots,x(11))^\\top\\in\\mathcal{F}=\\mathbb{R}^{12}\n", "$$ \n", "\n", "As we also discuss in other FMP notebooks (e.g., in the context of [music synchronization](../C3/C3S1_SpecLogFreq-Chromagram.html) or [content-based music retrieval](../C7/C7S2_CENS.html)), there are many different ways of computing chroma features. Furthermore, their properties can be adjusted by applying suitable postprocessing steps such as [logarithmic compression](../C3/C3S1_LogCompression.html), [normalization](../C3/C3S1_FeatureNormalization.html), or [smoothing](../C3/C3S1_FeatureSmoothing.html). To give some example, we compute in the following code cell three different chroma variants: \n", "\n", "* STFT-based chroma features (`librosa.feature.chroma_stft`). \n", "* Filter-bank decomposition using IIR elliptic filters (`librosa.iirt`), logarithmic compression, and chroma binning.\n", "* CQT-based chroma features (`librosa.feature.chroma_cqt`)\n", "\n", "For each variant, we use the same window length (`N=4096`) and hop size (`H=2048`). Furthermore, in each variant, we normalize the chroma vectors with respect to the [Euclidean norm](../C3/C3S1_FeatureNormalization.html) ($\\ell^2$-norm). In the following figure, the resulting chromagrams are visually superimposed with the manually generated chord annotations (in color). " ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "execution": { "iopub.execute_input": "2024-02-15T08:58:01.897339Z", "iopub.status.busy": "2024-02-15T08:58:01.897024Z", "iopub.status.idle": "2024-02-15T08:58:10.003666Z", "shell.execute_reply": "2024-02-15T08:58:10.003087Z" } }, "outputs": [ { "data": { "image/png": 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BeiZgbW8OAwqeSqq61LjvFZE/i0h/VW1QM2jxkvCgrqvLj/vX/ONuPnPcEVRVtY7101WrW/bWeNOdD+bcO2y7FVVVlSxaspS58xexzZiRuWv3PvwfnnvldS74RvqgZ/nv/f8FKBikLdPfns62E7Zl7sy5VHeups+APvXCfPhe82ezj97+aKr/5RddDsC+H9uXbXfdNveArtPGNcffr63l/dp8N/j+mWdSWVnZbNlak2mzZjFt1ixGDBrE9Nmzue2RpinWvfbaMwwZMorHH7+TNWtWsfPO+/Lyy08BMHLkNhx66ImNptHUgTphw4b13HnnlUya9FneeWcyPXv2oWvXHixduogBA4bQqVP2i8/rrz/HkiULGTRoBP/+d14Z7ye/u5JJh+3PTuO23qiX3fseebrxQJHnXnmd5155nVemvJP5yeiJZ1/iiWdfAsI3aVVFRApknL9wMf379m6xzA2xcO5CKior6N2vbdKHvBLn4vmL6VU3OPUFsTVJ6rAxNmzIXh1ZtGgec+fOpH//wVRU5O/duro6br/9Mj760U8wpCOYdN0EaI3R7jlgaxHZEvgQOBH4tA0gIoOAOaqqIrIHYcm9RR9fp384m4ee+B+Hf3QvHnriWUDYc5ft6dWze4uEn79wcYviWX76+6v43rln8PsrbwbgM8cdyeiRQ1m8ZBnPvVKsU9dyli5cSl1dHXdeeWfO75gzj2Hg8IG5322hRf7UPU8x5X9T+OSXPhkzaX4af/zHP/jKCSe0+QOqKfy2BcZQHnzwppw7GaQBPvig7QzkzJ07E1XlscduT70+bNgYPvrR46ipKVTa+s9/7gXCC0Yxdz7wOCtXrWbv3RpXssvivenNfxlsjl7Hj357BRd846yCgeZP19zCp489jC47DOOdV95hw/oNrF65mvETx2+0IuZtfwnb5k76+knMr53PssXL2GHiDg3GUVXq6uqa/fJ5659vZfz4Pdlrr8Pj93gtGARbi5tv/j0nnvjVRsO9884rmdfmzfuQO++8ks6da9htt4PYbrtdmTHjHR544EYAbrvtL8zZY2cO2nf3VpPbSadVzqOOy9mXAJXAVar6UxH5AoCqXioi5wBfBNYDq4BvqGqjr+VDhgzRz3/+802S4dRPHsmWI4ayYNFienTvRqfqapavWMnMWXPZdqtRmfFemPwGd//7qczrLeWCb5zFRb+5vNXTTSNZolZVrvhR2+9rHTh8IHNmtGxb1l477sj40aMZ3L9/o2/8F13ePvXXGuy887689tqznHDCV1ixYhldunSje/deuevJknZbcOihJzJyZPj8snr1Sv72t181GmePXbZn7JYjGDNqGCtXraaqqpJO1dXMmbeA2jnzG/yc0x79+vOnfIJ7Hn6KmbPmNho26xNNU1izag3X/fK61GsnnHsCPfv0zP1eu2YtK5evpHe/3tx3/X3MfDe8fPTs05OjzjiKbj26ZeaTrE4ljBu3G6+/HlbO9tjjYHbcce9694OqsmjRXPr2HUhTKO5ju+9+EGPGjGfatDcYOXIb1q5dQ//+g1FVVJWKiopW6Zff/PzJ/N9fbwDgtOM/xqjhpT10RHoOfUFVdyupEK1MqwzUbUVzBupi9tl9J/7zXHhb7N6tK1869TgqKip46MlnGTlsMOO2Hk1lZQX/e3lKs5byypHDTz6c+2/YNA8V2WP77enepQsLlixhcP/+rFqzhgN23RXYtAbqNA499EQ6derM4MGj2nSgTjj99O/w/vtv8PjjdzQ5Tv++vRtcVfreuWdQWVlZMIi01wtoU9n9wN3p3qs769etZ9b0WUydPJWxO4+ld//ebLPLNiCwaM4iBo4YWG9Fp3gATWPS5yYhCHdccQcAO0zcgVefebVeuP2O2Y+ho4fSvVfh6t7SRUu5+fc3N5rP3nsfwahR29KtW3g5mDz5aZ599iEOOOBYRo/ensrK7AXQxYvnc8stf2o0j/Zi9IihnHDMIVRVVbb7KpoP1O3MxgzUTUFENmlLYx2V/r17s8f223Pvf/7TeGCngLbq05WVFZz88SOYv3Ax9z6y6bfLyd88mepO1Vzz82taPe1jzjyGd197lyn/m9Ki+GPH7sz48RN54om7mD+/vm7D3nsfwbhxu7Nq1Qrq6jagqjzxxF3U1ratlcCN5YwTjqZfn165/fWz5y6gf99eraZvlOADdVYiIocDvyMsfV+hqhcXXZd4/UhgJXC6qr7YWLptPVA7juM4pefjR3yULYcP5sXX3uKxp19g8MD+nPKJI3j06Rd4ecpbrF+/gRFDBzJy2BAWLFzMshUrOeLAvRm8Rf96aflAnZZAsEz2NsYyGXCStUwWv2F/hTBQ7wn8TlX3bCxtH6gdx3Gc5nDhhRd2uIG6vSyTTQKu08AzQG8RGdwKeTuO4zhOh6a9LJM11XpZgWWyVpDNcRzHcTZpWmOgboplsqaECZ6ql6nqbh1t6cJxHMdxWkJrDNSNWiZrYhjHcRzHaTHDBm9RahHahHaxTAbcBZwjIjcRlMmWqKofYOs4jrORdO3anZUrm3+4UHtxwN678tjTLxT47TJ+G+bOX8SHs+fStUsNB+y1K7uM34aqqryVtoWLl/KHq+rvP//cp49l6KDsY08/9/kvtZ7wZcJGD9Squj5aHnuAvGWyKdYyGXAvQeN7KmF71hkbm6+Tp1uPbqxYtqLxgGXO/hMm8PiLL9KnRw9OOuww/nzrraUWqcVMmvRZ1q5dw7Bh4QCQ9jB4MnToGHbZ5SPcffc1bZbHpMP2584HHm+z9DsCux+4O0sWLmHsTmPpM7BPvZPxmsJJJ32dd999lYUL5zJ69DgefPAmhg4dzdixOzNixFiqqzshImzYsJ7Kyipqa6dxzz3XNpru2LE706fPFmy33a48/vidvP9+65k4Thg9cigH7rN7wWC6/8QJzJ63gP59ehcMxg3Rt3dPvvOV0/nbrffmLNR955zT6dSputVlLndaZae5qt5LGIyt36XGrcCXWyOvpvKxg/cF4J5/P8X4bcZw3McOZMXKVcydv5BpM2bRqVM1EyeM5/nJb3D/o/9tT9FajW133ZY9D96TTjV5W8dNsbRULuy9445sM3IkwwcOzFm+2mnrrenetSvVrWwEoT3Yf/9JDBu2FV27tszu/MZw/PFfpnfv+ntKi/nkUQdx690Pp17ba9cd+O8LweLWaccfxajhYWPGw089R5eazjn74P9+8n+sWLmqlSRP5/ijDuKWDDl3PWBXXnjshXr+4/ccz6CRgxix9QgqqyqZM2MOA4YMoMKcGLZhwwYevPFBRm4zknG7j2Pqq1N59J/ph9Kc8q1T6Nylc6plrZv/cDNLFy7lcz/8HBs2bGDB7AX06d+H6s7V9UyBHnT8QTx8S3pZPvKRo5kzZwYjRoxl+PCtee+9KfTq1Y/u3Xuy00775MJlnYaVWCsbPHhk6vWE/v0HM3LktkyYsF/Ob6+9Dm+1gbpfn16cc8anGgwzaEC/Zqfbqbqaz57kJ3htek/DyP4TJ9C1aw1jRg6lX5/eOf+169ZRVZk3W7fbjtvlrnXr2oUtRwxlyxFG4byNDJP169OLBYuW5H4fe/j+uaM6W4ODPnkQo7cfXc+/a4+urFzWtscDVlZVsmF9889o7tK5M+edeioz5sxhUL9+qYNxn549U2KWH4cffjLDh2/Fgw/ezAcfvMmnP/0NunVr36OEPvvZH7By5TKWLl1YMEgfeOBxqCozZrzDwIHDWbbwbcaNHZ071W37b4zm/em1LFi0mN12GleQ5qH7T6yXT/GhCztvPzZnnrctOGjf3Rk3djSjhr/OtBmFX8g+8flP0G9QP3bce0eu/tnVOf8zvndGPQtX9rCahMrKSo445Yjc76122IrVK1czevvRrFm1hsfveJyJh01k0IhBDcp4/JePp25DHSJCVVUVA4dl2+MePW40D1N/oD7hhK/Qs2dftt12Qs5v7NidGsw3i4bs5mcN8s3tr127dufYY8/i0Uf/yaxZ4bz6k449jOFDBlJV4pPxOjobNVCLSF/gZmAUMA34lKouSgk3DVgGbADWN1Wju3evfEf6+lmfZt7CRYwZOazBOJ2qm7csUl3d8ioYO3oEJx17GLVz5nHH/Y8xb8Hi3LUvnnocP/ndVbnfO263dZMH6rE7j2XiYRO58bc3sm7tuoJrQ7YcwhGnHJFpP3fIlkOYOnlq8wvTRA74+AGMHjeaq356VYPhdh47loH9+jF0wAA6VVUxb/FithwSjPUPH9i0Qwbaki2HDKFzdTVjR47krieeaHK8sWN3Ljij99BDT2gL8TLZdttd6dGjN9tuuysVFRV0796r4PAPgDFjxgOw1VbhBKghPervhNxyxBC2HNGywxOGDGx85t5SPnfSJIbEJdO9dt2x3kDdb1CYlVVVV3HG984ADe6NYfyeob66du/KsWcd26Q4FRUVLbJhPWb8GCbudGybrLpss80uvPVWOC70c5/7IatWrWg0n332+Rj/+c899fzPOuuC3Oea3Xc/iHHjds8drTp27M7MmvUBEybsz9jRI1q5FE4aGzujPh94WFUvFpHz4+9vZ4T9aGPnTxfTtUsNxx91EFuNGk6nTtX0bOB0mpbS2MBfzNBBA/hw9jx233kcRx4YlqaGDBzAl047nh9fcgV1dcq5nz2ByspKvnfuGTz69Avss/tOTT7/157Yc9r5p/Hqf1/l2YeeBWDC/hPY9YBdG4w/cNjANhuojz3rWAYMGUDdhsbPo560//6FcvVr/rJXa3LSoYfSu0cPZi1YwNbDh9O1piZ3rbGB+rDDTqK6ujOvvvoMEyce2qL8a2q6snp101Y6DjvspNxRgkcffQZdu3bn5pv/wPDhW/ORjxzVovzLjVM+cQS33ftIvfPg+/XplbtXtho1jIED+jJxwg7c+cDjfOa4IwvCtraN6PZgz0P2pOvytvk0Ul2d/wQmIk16GbAvG8cf/2W6ds1Pjk499dtUV1fXO4Zzq612ZP36dWyzzQSgZafoOc1jY3v6JOCA6L4WeIzsgbpFjBtbf3m3VWna+MnJHz+cIYMG0LVLDWvXrUuduf/ga58r+F1VVcUh+zVqKZWTvnYS3Xp2qzeYiwg77r0jg0cNprKykr4D+zaa1ra7bst/7m39QxNOPe9UOncJb9RSIQweNZhZ08JsZ8TYEQyo6Mq+O+/MA888w+BWGpT3HD+eZ197rcnhLzjrLFauXs2v/laovPPDz30uV7db9K1fhwP79mXOwoX1/E8++RsFD67GvgM2xAknnMu1117caLjq6s6MGDE293vQoDBjOeig4xk6tI3vhSbSs0f6ALDL+G146bXC87k/fvgB9OvTiw9nz6N2zjxeef0dLvhGOJbyvC+dmjuJ68gD92FDXR01NZ1zcSsqKvjCZ44DwnI7bLp7Ok/86om8//r7dOvZDdpIQXvXXT/K7NnTmTjxsCbH6dw5f5Z5sY5D5841xcGB0C7jxvkZ1O3Jxg7UA5NtVqo6S0SyNrEp8KCIKPBXVb0sK0ERORs4G2DE8FTjZa1Kz+5Nm6WPHjk09/bZ3OX1hvjcDz/X6Gx7wJDsrQjFNHXmXixDco51l25dOPqMo6npVkOnzp1Yv2491UValiLCUacVzuyGxCfoCYcc0uz8s+jRtWuTw35k550BCmbKAN//7GcbrZMzjj6ai6/Na8xuPXw4Bxx+ZtMFbQLJsmFD7LrrRxk7dufoPoDhw7fOXRs9elxGrPYna6/q9tuM5mMH7ctPfnclI4cN5vRP5fvI0Bjn2MMPKIhz+qeO4obb72f8tmPoUtN4HW2q9Ojdgx333rFN8+jUqTMf//jZzYqTKKO15LnhtB+NDtQi8m8gTbPie83IZx9VrY0D+UMi8qaqpq43xkH8MoDdJuzU5mdQNqWDJjOA1ub4ow5u9xvk6DOO5u2X32b09qNZOGchA4YOQEQ47fzT+Odf/8mBxx1Ir375b57Fg3R7klU3240axRvTpnHs/vtzx+Phu/9eO+xQL5ydSTdE506dGNC7N/MWL+aAXXdl/wkT2nXm1rNnXyZN+iw1NfkXkwkT9m8gRukZtEU/Zs9dUOA3esRQRKRZ98vIYYP57ld8t2apSGbRe+99RCMhnVLS6ECtqgdnXROROSIyOM6mBwNzM9Kojf/nisjthIM8mq7B08Yc97EDue2eRwA454zj+ePVt9Ctaxc+c9yRdO2SvvzTEj528L7c8++nAPj+Vz9LZWVFuwwInznvM9z3t/s46PiD6NmnZ06jddiY/Pf5Tp07ceK5J7aDNE2nWCt8/wkTGD9mDP169WL+4sX069WLN6ZNY+8dd6RLzca106cPP5xX3nmH/XbZZaPSaYgtthjG3Lkzc78HDhzOnDkz2GefIwsG6U2Bz5/yCZYuW86b735AXZ2yx87jfFa2CdKzZx9OO+38gu/bTvmxsUvfdwGnARfH/3cWBxCRbkCFqi6L7kOBH21kvq3KdlttCcAxh+5Hn149GTigLx/dezcGDmj8m3Bz2KJfn5y7rZ5paQ/Lmi41fPzsj7dNhm3IhG22YfnKlTzxUtBkPWDXvCLdgD6hLk88tL5y1xeOO45lK1Y0a+Do3aMH+0+Y0HjAjeCoo07n6afvY8cd92LevFp69uzLI4/cxhZbNE+hsVzo2aM7e+y8fanFcDaSpnyWcUrLxg7UFwP/EJHPAtOB4wFEZAhwhaoeCQwEbo8PzSrg76p6/0bm26pUVlYULNclCixtSUu2drSELcdt2S75tAWVlZV8dLfd2HGrrZg+p+napQP79mVgitJYqamsrMxpbffqFRTuTjzx3FKK5DjOJsBGDdSqugA4KMW/lmAyFFV9D2jZLv4OxrDBW7D7TuPYe/e2VSrp2bcnSxcuBeDA4w5s07zag369e9Ovd+9Si+E4jlMSNr2NiJswFRUVHHnQPo0H3EhO+MoJLF+ynFUrVrXbzN1xHMdpGySY4S5PRGQZ8FajATse/YFmGYfpIHi5Ny+83JsX7VXukara9D2tmwDlPqN+q6nmRjsSIvK8l3vzwcu9eeHldpqLr4s6juM4ThnjA7XjOI7jlDHlPlBnmhrt4Hi5Ny+83JsXXm6nWZS1MpnjOI7jbO6U+4zacRzHcTZrfKB2HMdxnDKmLAdqETlcRN4Skakicn6p5WkPRGS4iDwqIm+IyBQR+WqpZWpPRKRSRF4SkbtLLUt7ISK9ReRWEXkztvtepZapPRCRr8c+/pqI3CgirXfyTZkhIleJyFwRec349RWRh0Tknfi/T0NpbIpklPtXsa9PFpHbRaR3CUXcpCi7gVpEKoE/AUcA44CTRKR8DuNtO9YD31TV7YCJwJc3k3InfBV4o9RCtDO/A+5X1W0JZnY7fPlFZChwLrCbqo4HKoHyOratdbkGOLzI73zgYVXdGng4/u5oXEP9cj8EjFfVHYG3ge+0t1CbKmU3UBOOwJyqqu+p6lrgJmBSiWVqc1R1lqq+GN3LCA/toaWVqn0QkWHAx4ArSi1LeyEiPYH9gCsBVHWtqi4uqVDtRxXQRUSqgK7Qrsd/tyuq+gSwsMh7EnBtdF8LHNueMrUHaeVW1QdVdX38+QywaR4bVwLKcaAeCswwv2eymQxYCSIyCtgFeLbEorQXlwDnAXUllqM9GQ3MA66OS/5XxGNgOzSq+iHwa8Jpe7OAJar6YGmlancGquosCC/owBYllqcUnAncV2ohNhXKcaBOO0R4s9lDJiLdgduAr6nq0lLL09aIyFHAXFV9odSytDNVwATgL6q6C7CCjrkEWkD8HjsJ2BIYAnQTkVNKK5XTnojI9wif+m4otSybCuU4UM8Ehpvfw+jAS2MWEakmDNI3qOo/Sy1PO7EPcIyITCN85jhQRK4vrUjtwkxgpqomqya3Egbujs7BwPuqOk9V1wH/BPYusUztzRwRGQwQ/88tsTzthoicBhwFnKxuxKPJlONA/RywtYhsKSKdCIomd5VYpjZHRITwvfINVf1NqeVpL1T1O6o6TFVHEdr6EVXt8DMsVZ0NzBCRbaLXQcDrJRSpvZgOTBSRrrHPH8RmoERXxF3AadF9GnBnCWVpN0TkcODbwDGqurLU8mxKlN1AHZUNzgEeINzA/1DVKaWVql3YB/gMYUb5cvw7stRCOW3KV4AbRGQysDPws9KK0/bEFYRbgReBVwnPoA5rWlJEbgT+C2wjIjNF5LPAxcAhIvIOcEj83aHIKPcfgR7AQ/H5dmlJhdyEcBOijuM4jlPGlN2M2nEcx3GcPD5QO47jOE4Z4wO14ziO45QxPlA7juM4ThnjA7XjOI7jlDE+UDuO4zhOGeMDteM0gIj0M/vaZ4vIh9G9XET+3EZ5fk1ETm2LtFuCiEwTkf4NXL9JRLZuT5kcZ3PC91E7ThMRkQuB5ar66zbMo4pgDGSCOWmopETzrrup6vyM6/sDp6jqWe0qmONsJviM2nFagIgcICJ3R/eFInKtiDwYZ5+fEJFfisirInJ/tOGOiOwqIo+LyAsi8kBi77mIA4EXk0FaRM4VkddFZLKI3BT9uonIVSLyXDx5a1L0rxSRX8d8J4vIV6L/QTHcqzFe5+g/TUQuEpEX47Vto3+/WJaXROSvxINyYr73iMgrIvKaiJwQZX4SODi+ZDiO08r4QO04rcMYwpnak4DrgUdVdQdgFfCxOFj/Afikqu4KXAX8NCWdfQB7ktj5wC6quiPwhej3PYJN9N2BjwK/ikdknk04lSoJf4OI1ADXACdEeaqAL5r056vqBOAvwLei3wXAU/FUr7uAEdH/cKBWVXdS1fHA/QCqWgdMBXZqToU5jtM0fKB2nNbhvnga1KtAJXEQi79HAdsA44l2joHvE06GK2Yw4ZzqhMmEAfcUwtGAAIcC58d0HgNqCIPpwcClyWxcVRfGfN9X1bdj3GuB/Uz6ySltL0Q5idevj2ncAywyZTlYRH4hIh9R1SUmnbmEYysdx2llfKnKcVqHNRBmlyKyzhzhV0e4zwSYoqp7NZLOKsLAm/AxwsB5DPADEdk+pnWcqr5lI8bTqIqVTtLOd68nN7CBwudBPeUVVX1bRHYFjgR+LiIPquqP4uWaKLvjOK2Mz6gdp314CxggIntBOHs8DrrFvAFsFcNUAMNV9VHgPKA30J1wstxX4sCMiOwS4z4IfCH5ViwifYE3gVEislUM8xng8UZkfQI4OaZxBNAnuocAK1X1euDXFJ6fPRbYHE65c5x2xwdqx2kHVHUt8EngFyLyCvAysHdK0PvIL01XAteLyKvAS8BvVXUx8GOgGpgsIq/F3wBXEM57nhzz+LSqrgbOAG6J6dQBjR0veBGwn4i8SFhmnx79dwD+F5fcvwf8BEBEBgKrVHVW02rDcZzm4NuzHKfMEJHbgfNU9Z1Sy9IUROTrwFJVvbLUsjhOR8Rn1I5TfpxPUCrbVFhMUFJzHKcN8Bm14ziO45QxPqN2HMdxnDLGB2rHcRzHKWN8oHYcx3GcMsYHasdxHMcpY3ygdhzHcZwyxgdqx3EcxyljfKB2HMdxnDLGB2rHcRzHKWN8oHYcx3GcMsYH6hIgItNE5OASy/CYiHyuBfHUnMTkNICIDBCRt0SkJv4eKCJPiMgyEfm/UsvnOO2JiHQWkTdFZItSy7KpsVkO1CKyr4g8LSJLRGShiPxHRHYXke+KyPL4t1pENpjfU2JcFZEVxn+xiFxqfq8VkXXm932lLq9TMs4Hro4nWAGcDcwHeqrqNzcmYRG5RkR+srECtiUtfRlshXzPEZHnRWSNiFyTcv2gOGCsFJFHRWRkA2mNEpF7RWSRiMwWkT8mx4imhD1dRJ5K8d/oF3MRuTA+e443flXRb9TGpJ2SV2b9ici4eG1R/Pu3iIwz13uLyLUiMjf+XZhcU9U1wFXAt1tT3s2BzW6gFpGewN3AH4C+wFDCsX5rVPVnqtpdVbsDXwD+m/xWVXt28E7Gv7eqfsHE+xlws7l+RDsXsWzIeqCVA20tm4h0Bk4DrjfeI4HXtQwM7G9s+cu5bYFawhGcVxVfEJH+wD+BHxDu/+eBmxtI68/AXMIhKTsD+wNfal1xm8xC4EciUtnG+WTWX7z2SULd9QfuAm4y138LdAVGAXsAnxGRM8z1vwOnxfvDaSKb3UBNOOAeVb1RVTeo6ipVfVBVJ7ezHLuLyOvxrfRqszzaR0TuFpF58drdIjIsiRTf2t+Ly6fvi8jJ5tqZIvJGjPeAnSmIyCFxFrFERP4ISJZgIlIZVxfejfm8ICLDTZCDReSdmM+fRESMbP8Rkd+KyELgQhHpJSLXxfJ8ICLfF5GKlPCLY7n2jv4z4hv5aUauj4nISyKyNF6/sEjuU2MeC0TkB3YmE2ckt4rI9SKyFDhdRPYQkf/GvGfF2VInk56KyJdiWZeJyI9FZEyMs1RE/mHDF7EnsFhVZ8a0riEM3OdJWGk5WEQqROT8WM8LYnp9Tf63SJjFLZGwZL599D8bONmk9S8j71Ymfm7WLSIHiMhMEfm2iMwGrm4s/6K6TYuf2VdF5KfAR4A/Rhn/GP23FZGHJKxkvSUin8qovxajqv9U1TuABSmXPwFMUdVb4krHhcBOIrJtRnJbAv9Q1dWqOhu4H9g+I2yjiMgrkl9tWx7b7IAmRr8fWAuc0tL8m0JD9aeqi1V1WnzZFGADYD+FHQ38UlVXquo04ErgTBN/JrAImNh2Jeh4bI4D9dvABgnLM0eISJ8SyXEycBgwhvDy8P3oXwFcTZh9jQBWAclDrhvwe+AIVe0B7A28HK8dC3yX8CAaADwJ3Biv9Qdui3n0B94F9mlAtm8AJwFHAj0JN9pKc/0oYHdgJ+BTsRwJewLvAVsAPyWsXPQCRhNmI6cCZxSFnwz0I7xt3xTT3orwQPqjiHSPYVfE+L2BjwFfjOVGwvLbnwn1OjjmObSoXJOAW2P8GwgPma/HOtkLOIj6s6XDgV0JD5bzgMtiHsOB8bGe0tgBeCv5oaqnxzx/GVda/g2cCxwb62UI4QH2J5PGfcDWhLp8McZHVS8rSuvoDBmKGUSYCY0kLMM3ln9j8TP7qqp+j9AHz4kynhP770OEdt6CUHd/Tl5AihGRP8eXqLS/lr5Ybw+8kvxQ1RWE+yFr8P0dcKKIdBWRocARhAGzRahqbjWOcJ+9RWjbJkUnrARcICLVjQVuo/pL0l4MrCbc3z8rvlzkHl90/Q3Cs8NpKqq62f0B2wHXADOB9YTlm4FFYU4HnkqJq8BSwhm8i4HfF12/ELi+kfynAV8wv48E3s0IuzOwKLq7xTyPA7oUhbsP+Kz5XUEYXEcSBrdnzDWJZf9cRp5vAZMyrimwr/n9D+B8U2fTzbVKYA0wzvh9HnjMhH/HXNshpj/Q+C0Ads6Q5RLgt9H9Q+BGc60rYfZxsGmXJxppl68BtxeVdR/z+wXg2+b3/wGXZKT1PeCmIr9rgJ+Y328AB5nfg4F1QFVKer2jPL3S0jLybpWWH3BArI+aFuZfL35DfTX+fsz2MeAE4MmiOH8FLmioXVr6R1i+vabI70rg4iK//wCnZ6SxXWz39bF+ryEeD5wS9vQYbnHRX13SD03YfQlL6mObWJYLic8V4Fngi0BVlGlUe9Vf0fVuhBfbjxm/6wmfFnoQXrbfJXxWtPFuAH7YFjJ31L/NcUaNqr6hqqer6jDC294QwkO/qUzQ8G26t6qe21BAEbnPLHOdbC7NMO4PogzEN/e/xiXcpcATQG8RqdTw9n8C4fv5LBG5xyzZjQR+l7wxE75nCWFWOcTmp+FusfkXM5xwg2Ux27hXAt3Nb5tuf6BTLJ8tq53pzjHuVVG+Yr/uACKypwTln3kisoRQD/1juOIyrqT+0l1BmUVkbFyunR3r+mcmvSz5UmVLYRHhYdUQI4HbTZu9QZjlD5Tw+eHiuCy9lPByR4p8zWGe5hXbGsy/KfEb6qsZ8UcCe9qZHWF1YtBGlKm5LCesEll6AsuKA0r4RPMAYeDpRqj7PsAvGkj/GfNs6K2qvYHpRekOJ7zgnqaqb7egDN8nvAjWtCBuqxGfR5cC10lek/tcwn3xDnAnYVVvZlHUHoQXGKeJbJYDtUVV3yS8JRcvz7RW+kdoXrHsBnPJfvMdQVDSAPgmsA2wp6r2BPaL/hLTe0BVDyHMft4ELo/XZwCfL3pIdFHVp4FZNj8RkaL8i5lBWJJvCVZRaj5hhma1akcAH7Yw7b8TVj+Gq2ovwkMiWWabBdhv+V0Iy+lZsgH8hVCHW8e6/i5kf7tvJpOJ+hANMIPwGcO2WY2qfgh8mrBUfzBhGX9UjJPIl6aQtpKwkpBQPAAWx2ko/zSK4zfYVzPye7wov+6q+sW0zKRwN0Xx35QMGRtjCmbZNS7Hj4n+xfQl3Cd/VNU1qrqAsNR/ZAvzTvrlHYSVmBbtCFHVh4CpNKLU1kb1V0wFoc8NjbItVNWTVXWQBgXcCuB/RXG2w3x+cBpnsxuoozLLN43Sy3DCt7Jn2lmUL4vIMAnKO98lr3nag/BGujheuyCJIGEf7jHx4bKGMDvYEC9fCnxH8gpHvSS/leMeYHsR+YQEbd1zaXgWcwXwYxHZWgI7ikjxoNcoqrqBMHP4qYj0kKDc9g0KNaGbQw9goaquFpE9CINZwq3A0RKU0ToRNPkbG3R7ED5jLI8rE6kDRgv5H2F2Wfyd3HIpoW5GQm7f9SQj2xrCqkBX6n8HnEP47m95Gfh0nI0fTvj23BAN5d8UMvtqhox3A2NF5DMiUh3/dheR7dISV7ObIuUvU6FLwralGsKnl0oRqZG8lvrtwHgROS6G+SEwOb6wF+c/H3ifoAtRJSK9CQqBGzPIXAW8qaq/TJF7moic3sR0vkfQmcikLepPglLqLrGP9QR+Q1g9eiNeHyMi/eL1Iwi6DD8xaQ8lvAC19/N2k2azG6gJS1x7As+KyApCh3mNMDtoT/4OPEhQvHqPfGe+BOhCmI0+Q6HiSgVBzlrC0nZuq4iq3k5YkrspLkO+RlB8SR44xwMXEx78WxO+y2XxG8IA+yBhILsyytQSvkJQAnsPeCqWO23bR1P4EmF7yjLCA/YfyQVVnRLzuokwu15G+Aa4poH0vkUY7JcRViYa2qbTLFR1LWGlpiEN3d8RVggejGV6htA3Aa4jfCb4EHid+g+2K4FxcQn5juj3VYLW7WLCkvIdNExD+TeFS8juq0n6n5SgEf57VV0GHAqcSOjDswl9trW36nyf8AJxPqH+V0U/VHUeQcfjp4QBZs8oDwASdjvYme4nCAqF8wiz2PUEBcSWciLw8aLZ7Ufiy2U/mjiAqep/qD9TbS0y64+gK3EjsITweWwr4HDzSWRX4FXCPfVz4OR4byZ8GrhWw55qp4lI+FzpOB0LCZriiwnL2u+XSIZE+34XVV1VChmcTQMR2Rf4sqpm7SLY5JGwd/oVYD9VnVtqeTYlfKB2OgwicjTwMGHJ+/8Is6UJ6p3ccZxNmM1x6dvpuEwiLKnWEpb3T/RB2nGc9kJErpJgqOm1jOsiIr8XkakiMllEJjQpXX+OOY7jOM7GIyL7EZR8r1PVejuJRORIgi7NkYQVv9+paqN6IT6jdhzHcZxWQFWfICj6ZjGJMIirqj5D2BkyuLF0282wvohsIGgDJvZhz4l7fDPp0aOHbtE/WPisrMi/U1RW5cWWuAMnbA0O1Gldzq11ebeYNOwOz3Xr11k5TdqBiopKcz0fr86sRtiVCZuGzafOyJK4169fz7oU8xBZ6VWYMmzYsKFemCpTN5ohn920VLfB1I+k72aSiuCvdfk0bD6Ju3pddhoW2yZJirZc1r12TV45tLLStoOk+q9fvz7vjvVj08uSz8pUUVnJOoKFRlttlZX132ttm9p8mhImy50qX8bKV0G/N/msMfXWuap+PWSnbftJPu0qU8erVuVtpiR9I6uPWn+bT53tu7Yeghl4NtTlr28wbbpG8mnY/m+x+astj9a/XmHa1PYdG60p7ZqUzYa113v2MjZW1ubNwxc8KlLu16z2zZJjQ8b9XNjXgn/W/ZTVZta9enXoA9WV66iuMtZM026vgi5l8qnIeFbY7ijWKQ3GS55PH86ey6JFi5pkD0FEmrukPIVgOjXhMg0mfZvKUAoNL82MfrMaitSeJ+CsUtWdAUTkMILqfoP7PAcMGMAvfxh2QnTvnjcA1bdv/tyApANWV+c7i31IrVqVV7atqckb8rGdbs6cvLEpOwAlaXfr1i31+tq1a1PdVhabz8qVeXPZiVzz5s2jNtqvsp143br8y0PnzvndK1265HdJLV68OOfu1Cnc/P375w1XWZnsQ8jms2LFinppQOHNn+Rv69Xm069f2GI9pDY7DZunTSepH1su207Tp+eNOvXsmX/Y2Tru1atXzj1//vyce9GiRQB07do1NZ6Vz9Z39+7dqQ2G4goGgx49etSLZ8ti5bblXb58eUHaCbbMVsaEghcW05Y2/6x+/+67ecNyW/ZfkJkHFJbd9lf7IO/TJ28Sf8qU/G6b6k4h/86d0vuolc/ms2xZ3hCYrbck7tKlS3N+STsCvFuVv59tvdp2sverravkHrD1kLQpwIIFC1Lj2fQstgxJeJu2le/www/PR6wdknNmvXgndWWv2/a12Hys29a9lSupY1t2+1yz8exzw7bf228Hg2pDetQycGDekF0ib9YAb9OuNs8KS9bkqjrK2CkjXlI/nzyjQWOR9bD9vDE2bNiwWlV3a1YGhTTyKpNOqY6q60nYw+g4juM4JUFEmjtQb2yWMym0CjmMvFXKTNpzoO4iIi8T7NMOBg5MCyThCL+zIT9TcxzHcZzWRkQKVhMaw65stZC7gHNE5CaCMtkSVW1w2RtKt/S9F8GQ+/ji7TNxvf8ygNGjR7tKuuM4jtNmNGdG3RgiciPhpLn+IjKTYFa3GkBVLwXuJWh8TyXY5j8jPaVCSrL0rar/lXBG8gCCmUfHcRzHaVeaO6NujMYsy8WJ6Zebm25JBup4AEIl9Y8hLKB79+5M3GsiUKhxbMkpL5jv8evX5RUgqqrzRexSk1dysW9Rgwblz6ewSiRJA1olJqsklGg+2rDFzJqVX9Ww4RMFlU6dOuWUyaxM1r140eKcu0B71JAsTCxcmN8ZYLUxu3bLK5NkabFaxRGrEJQoaViZrGJLUpYhnZumIWsV/Bpj6ND8mRZWJiuLrfsRI0bk3Em7ppUFCpWYlixZknN36tSJ9YRT+9IUA61SjU3bLg7ZMFZpxyr1ZO0CSLBKhFnY8Pb72dZbb51zb9k/KF9maSRnfXdLFMUAqirzdbz33nvn3KtWh7bs3i2vJGfrNU1BqlgWq8SVtI9VGBq7Tf4QsrED0k3O23qwZbPpJGFs37H3pG1Le5/b/pWlEJekaePZvO1zoGtGndhl1cQ/Tf7ifOwnwi233DJV7rRdA7YNbD62frLab7fdgj7VkB6Fn1eTuPY+s2nYPm3ztLJYua1SYaI0au9JS5JPU+6bhOZ+oy4VpfhGDUHz7bR4upLjOI7jlITWnFG3Fe0moaqW/2uL4ziOs9ngM2rHcRzHKWNa+xt1W1H+EjqO4zhOG+EzasdxHMcpU3xG3QpUVFRQ0zlokFqNR6v1l2a7eSV5U51W0ztLG9Bqe1ot0EQT0Tak1WC0b2JZGpTWvN6atXlNyIULgnb2mjVrSHRHs8xoduqcbpbTmqNMzIkWmK5ckTcnaO2ZZ5lStdgyJ3navK2pzsS9rrbQJKltM1uvViM00eq05lCtTBMnTsy5587N7+Szadgy274xeHCwdW9NUFrN1SwNfoBa6msIJ+YZbRrWvKTV2rUawXbXgHXbukrysWZmC/pAhmnWLA1Yq/2b2LOurEjX5rX5WLI0+NO0um1YW2e2LW2fsmGsSeCkTay2r7UtXleXv4ezzKdmmc5M+liWqVd7/2VpIWeZCk7ytyY87fPm9ddfz8vx+jRTnnQzu0m/sv3F5m1lsm1ty2Cx/TvJ06Znd2JYk7xZ5mWTNlu/fn3BroakTrLMKKedEVCMrXubTlr4rJ0MTcW/UTuO4zhOGeMzasdxHMcpc3xG7TiO4zhlis+oHcdxHKeM8W/UjuM4jlPm+IzacRzHccoUn1G3AmvXrs0Zs7fbL+z2gbQtVGvXpZ8ZarcgWLX+999/P+e22wGSLTF2q4g9qMCcA1KwfcZuR7DbYOx2nESW3r17k+yoSMsbCpUd7BYEu70i2ZYxZ86c1DLarSI2Pevf2GEZNl7aAR7V1YVbRSxWlrQtdcXboxLsNie7DcTKYtOzdZ/0GbtFx17PkqlHz55siIdy1JjtfUmZbRvY+rP+WYdl2O0udute0n9tH7BltG2ddZiBDT9t2rScu2bd/AZltfdOspULCrdz2fLY7U3JdiR7sEbBDMXUa+eMPm37XdIONj1bJyuW5eXL2kJl29getpL427J8+OGHObfdJmbTyNoamrYl09aNjbf99tvn3F2Hjsm5O3dOP8AkyT+rn9u07bPRbmHM2rqXyGoP87B9ypK1zWrmzJkAdFm/qOC5lqSTdcCQrTPrtmmn3cPWP+v+S+6trENm0vCB2nEcx3HKnA639C0iE4E/ANsBnQhHVa5Q1fSzFx3HcRynTOmoM+o/AicCtwC7AacCW7W2UI7jOI7T1nTY7VmqOlVEKuNZ0leLyNNtIJfjOI7jtDkdcUa9UkQ6AS+LyC+BWUC3RuIgIgOB3wITgUXAWuCXqnp7M/N3HMdxnFaho86oP0P4Ln0O8HVgOHBcQxEkqCreAVyrqp+OfiOBYxrLbM2aNXzwwQdAofamPYwi0YS0xvCtZm3//v1zbqu9bRtn3rx5ObfVLE60mbMOrrDaqlbT0LqHDRuWKldyQECnTp2o7VH/utVmtBqZVgvSaj8mGquJNiYU1tP06dNT5bZYuW0+iUa21RIdNGhQvXJ1X1pYV1YD1tarLU9Sx1ZWm7dtG1snVuvV1oMt28KFC+vFs+1utcGtTCtXrGBFPNilri5dGzUtb/tmnqWFbNvY1ncio9XutmnbQ0tsvWZpfb/00ks59/ROYVeD3S2Rpk0LhYeM2Pytu0+fPvXKYLWnbdtYsjSLBwwYkHMnuyqWLM7f70k7ArxXnd8dYOW2bWnLacMk9WO17e1hGfYADKvtXFtbm1qGrbfeOudO6iHrkI9tt9025+5WMzjntv0kbadA1sEotp8kz0iAt99+O+e2fa137945d9quC3v/ZT2HrKxTp04FYGWP2oL6SerY1pPtU7bf2TazmuP2WW9l2WKLsBMj7bkH+ba2O0Uao0N+o1bVpEesAi5qYrQDgbWqemlROn9oTt6O4ziO05p0yBm1iBwF/BgYGeMKoI1ofW8PvNhiCR3HcRynjehwM2rgEuATwKuatR7cCCLyJ2Bfwix795TrZwNnQ+GyteM4juO0Jh1yRg3MAF5r5iA9BfMdW1W/LCL9gefTAqvqZcBlAGPGjGnRy4DjOI7jNEaH/EYNnAfcKyKPAzmtAFX9TQNxHgF+JiJfVNW/RL+uDYR3HMdxnDano86ofwosB2oIlskaRVVVRI4Ffisi5wHzgBXAtxuL26VLl5y2pNX+tVqdiQaj1Y60moVZy+c2vE3bLhYkWqD2jctqgy5bntcutHaRrZbj/Pnzc26r2ZjQt29fKqJCtNWwtG4ra5Y96URuq2lqNaCthmUWtuw27sqVQQPa1oPVtkzaY0hloeZslr3fNA35LO1pq2luNVezbCDbtBN70VamrJty4aJFOXeXmhrWEPLNaoeELBv0WXaZ7e6EDRvy/mma+Fa722pa23ZK0+AF2GabbXLu7eLOg+498pq9q1bltadXm7z79M3nI+TTtnVotYyTdrD52Tq2/dH2f1uvtg4TbeakzwEsWZrXAh49It/vbBq2HrLaONm9YONZG/O2r9n6ttrdNm2rKZ2QZa8/ObMAoOfyXqnh03YyWD9bRtv/bduMGzcu57b3aNr9YneI2Hqw/c720a5d88+1oUOHAjCkhxS0fU2XEFfNboksm/r2mWTr1T4LrMZ/cv9n2VtP+o7dYdIUOuKMuq+qHtrcTFR1FsGimeM4juOUBR11Rv1vETlUVR9sE2kcx3Ecp53YVL5Rp++mz+bLwP0islpElsW/pY3GchzHcZwypKqqqsl/TUFEDheRt0Rkqoicn3L9ABFZIiIvx78fNipjcwqkqs1b/Hccx3GcMqW1Z9QiUgn8CTgEmAk8JyJ3qerrRUGfVNWjmppusxfnReQYYL/48zFVvbu5aTiO4zhOqWmDb9R7AFNV9b2Y/k3AJKB4oG4WzbVMdjGwO3BD9PqqiOyrqvWm963BunXrmDt3LlBo29ZqyCYasPatyNp6tRqCVpvRYm3LFtgpjhqKarQQbRrW/nIXo8FoNRutVqLVJE06R5cuXVjdo37atjy2vLYerAZxolFr7SbbDmg1UK1WpNW4tWW32vJJnlbL1dpNT+qh6+Js7W2bdtqNkWaTGdLburg81h6wre9Ec9hqtFo5rNtqwK5Zs4ZahgCF5UmT38phtWytJm6WXWZbnkQWu2PAakxbLWhbXtvXbFyrIZ/kb+1nW83ngnZal26n294jVpa0XReWLI33rPBJ3Vr5Ew1jgLl9quqFhcJ2mjNnTqqsSf3Y58Po0aNzbquFbNOzbWnvYZtOUm8VndN3nzz9dP6QwQWvvpBz275h6yppY9u/srS+Bw/O2w4fMWJEzm37tL2PknTS7uFibP62zbbaKpxuXL16XkH4pN6yziewZbQy2ee0bVfb15P6ts+hNK3v5triauaMur+IWBsgl0XbHwlDCfZGEmYCe6aks5eIvALUAt9S1SkNZdrcV4kjgZ1VtQ5ARK4FXgLaZKB2HMdxnLaiBUvf81V1t4aSTPErfnN4ERipqstF5EjCoVVb14tlaK4yGUBv4+6VFchxHMdxyplk6bsVlclmEk6VTBhGmDXnUNWlqro8uu8FqqO1zkyaO6P+GfCSiDxKeHPYD/hOM9NwHMdxnLKglbdnPQdsLSJbAh8S7Id82gYQkUHAnGgMbA/ChHlBQ4k2eaAWkQqgDphI+E4twLdVdXZzSuE4juM45UBrK5Op6noROQd4AKgErlLVKSLyhXj9UuCTwBdFZD3hyOgTGzs/o8kSqmqdiJyjqv8A7mppQRzHcRynHGgLgydxOfveIr9LjfuPwB+bk2ZzXyUeEpFvATcT7HUnGS/MjhIQkQ3Aq8brJlW9uKE4q1atYsqUoAxntQKttmJi29lqYFpNQOtvNV2tJqfV/LRpv/fee/XSsPaKrUZplsbokCFDcm6rvZrYFa6rqyMJbjUcs7TcbRj7EpbUQ6fqvLaldSc2eItltZrFVjvTam0mWpa2Xp977rmce/r06aGstYVy2/BW29Nqtydvs7ZtrMavrW/bNjbtLJvmie3tLK15K9OwaA878V9PuGbrJHFbPyvHm2++mXNbzXpbXtsHbJ+2mukJtq0tWXbObdkTO+cALHsfKNTctvLZsFka9IuMLfQxY8bk3EkftLMS2y/tPZKl/WttbCf35fDh+c98PXrmZbXltflkudM0iG172LwtNg1br1nayQlp7QiF2tg7Ddg+NT2rzZzkn2YXHwr7se13yb1YLIvVuE/ce+6ZV0a2OxayNPLtcy1JY7ksz+3MgfRzAWpr859obfvZfmf7g7WZb5/TSR+0O1tsPSTta7X+G6OjmhA9M/7/svFTYHRK2GJWqerOzczPcRzHcdqMTcGEaHMtk23ZVoI4juM4TnvSUWfUiMjewCgbV1Wva0LULiLysvn9c1W9OSX9s4GzodC4gOM4juO0JpvKoRzNtUz2N2AM8DKQfDBRoCkDdZOWvqOVl8sARowY0TwTM47jOI7TRDrqjHo3YFxjquSO4ziOsynQ4WbUwGvAIGBWG8hSjx49erDXXnsBhdqCVvsxzRaz1aS02oT2/cJqGVvN1Gqj7dwlaocWaJoaa3Dr1q4z8fJ5WoR0LcfEtu7q1atZFEXMKqOVz2pj23Im7g9rP8z52TLasmfZFLdh0rRr7fVRo0bl3IkW8JDaQvmytJOtNmryNpt1s1hN4az2s+60erN9wGq5Y2TqbOSuq6vL2fq2GrCJrLbP2bJktVNWGdLq3vpl2bK27ixNYLvbYHjUorf1YNvDppc1u6izdsmNZnhV7HfdjGa0LaPVIrf5ZO02SNpy3ry8DekPZ+b79LyV+T6fpQFudwqk2YS3YbPsj9swBX3GkLYDI0uzffvt85re3ZZsnRomrV/ZHQu2LLbf2XvRam9b0uZWdjeL3QWQZVM/7f4b0qN/4bMl7i6p25AvyxrTLyszdmtk9Xtbx0k7pD3zId9+t//72dTraXSoGbWI/IuwxN0DeF1E/gfkeqSqHtOEZIq/Ud/fVod5OI7jOE5jdLRv1HcBA4Eni/z3J5hJaxRVLf/acBzHcTYrOsyMmnCe5ndVdbL1FJEVwAXAla0tmOM4juO0JR1tRj2qeJAGUNXnRWRU64rkOI7jOG1Ph/pGDdQ0cC1d08JxHMdxypyONKN+TkTOUtXLraeIfBZ4ofXFchzHcZy2paPNqL8G3C4iJ5MfmHcDOgEfbwO5gFCJyVaiZDsTFG5pSN6GsirbGni32xGsir/d3mPTTg4xsFtg7JYQu21j5eL81hxr6P/DD/O6drNn508ETcoza9YsansU+kHhVhubj5XbGqdP6snmbbdwzJgxI9XfbvGy27bstohk64aV76CDDsq5Bw0aBED37oVbXGy92TpO236RtU3MymS3itj0suIm2+FsebO2C9ntKZVVVaxnXSxT3j/J0+Zn5bB9zWK3C02enP+CZOszkStr65XtA7aOs8pjtwNVrAh9cMVKc6DMinx/tYfS2Dwttg7toSlz4wEItux2y1PWVqktttgi5541K7/bM+1+t/fNC8vzYbP6mr1HbV9L/O2hDbvuumvO/e6776bKbQ+GsPnYciZp2+eH7RtnnnkmaVj50g6uydri2Llzvi7Xmm2iVqasrV3dunUD4I033sj52X5kD2+x/kk8gIkTJwb5Vs4p6Ju5w4FStrkCLFyYP78p6zlk6d+/f70y2K17ts8PHjw4yNSMGXKH+katqnOAvUXko8D46H2Pqj7SZpI5juM4ThvS0WbUAKjqo8CjbSSL4ziO47QrHWZG7TiO4zgdjQ619O04juM4HY0OufTtOI7jOB0Jn1FvJOvWraO2thaA7t275/yzNIET1hvNTKvBmGagv6EwSQPa/CTlkAYAa/Leal5Onz4957bakXvsuScA1VVVOa1vm57tPFaz0R78YA/8SLCavVbrdM+YHxRqj9p81q/La51WVedlSerEaqJbbfZEk7NmYfaBKTZPe3BBEt62h83Hahjb9LI0fi1Jfdt6tWlbDfFFixbl3NXV1Symawyflyspg03Paq7afmJlstq8Vot14MCBOXfaW72NZ/ulddt8bBls2sN6hXJmaZRnHdCRpU1v75fkvrQy2YMh7C4Ei03b1mGi0Tts2LCc35ittsq5JwzvnXPb+rZ1ZTXG0/qg7WuWRJMZCrWQbdvYg3VsH7Qa0Qk2H3vfdpf69y0U3q+J3GmHwkD2AT6J5jNkH6iRpGnTK9j1kPHssbtmkjD9e6xNfQZbbNtYDXrbT2x923qzeSb3sx0L0vqlza8xfEbtOI7jOGWMf6N2HMdxnDLHZ9SO4ziOU6b4jNpxHMdxyhj/Rl2EiAwCLgF2B9YA04Cvqerb7SWD4ziO41h8Rh2RoKJ4O3Ctqp4Y/XYGBgKZA/XixYt5+OGHgUJbwnPnzs25Ew0/qx1sNRitJmyWZuh2222XGiaxQWy1Km2jDh06NOe22onWxq7V9LYy9unTJyfrypik1Yi0clgtSCtLmtvmbTUibdoWG0Yq8pqkaZqc9s0z0caHvCbskNpCjeAsrW9bJ4nb+lk7y/Pnz8+5bdlsGJu2dSc2yG1d2vJaty3vmjVrqGVhvXyStu/atWvOz6ZtNcptPKu5u5XRYLbtmmYr2tal1Sq2clts/vYeeZu3cuVKsBrGVr6lS5fm01uXT697t7ymre13iVx2p0OWjXerrWvzsdrbSTq279i+sWZVfc3o4jJkaXUn7Wfb2tqetlrIto6zzgWw6SRay1la0vZ+effdfD+2zxNbD8kzzNo5t/3Ouq3GdPJcgcL71dZPgu2jadrVUKiRb/tjsutjSI/agrhJ+9l71crUydgo792rd2p5bF+yZfiwNuS5cEHD9sJt328Mn1EX8lFgnapemnio6svtlLfjOI7j1GNT+UZd/zWrbRiPH4fpOI7jlBHJjLqpf01M83AReUtEporI+SnXRUR+H69PFpEJjaVZdnN+ETkbOBsKl7Adx3Ecp7VpzRm1iFQCfwIOAWYCz4nIXar6ugl2BLB1/NsT+Ev8n0l7zainALs2GgpQ1ctUdTdV3c1+K3Ecx3Gc1qQNZtR7AFNV9T1VXQvcBEwqCjMJuE4DzwC9RWRwcUKW9hqoHwE6i8hZiYeI7C4i+7dT/o7jOI5TQPKNuql/TWAoMMP8nhn9mhumgHZZ+lZVFZGPA5fENfvVxO1ZDcWbO3fu/It+c/kKYH5D4Too/fFyby5sjmUGL/fmRnuVe2RTA77wwgsPVFZW9m88ZI4aEXne/L5MVS8zv9MMuRdv02hKmALa7Ru1qtYCn2pmnAEi8ryq7tZGYpUtXu7Nh82xzODlLrUc7U05lltVD2/lJGcCw83vYUBtC8IU0F5L347jOI7T0XkO2FpEthSRTsCJwF1FYe4CTo3a3xOBJao6q6FEy07r23Ecx3E2RVR1vYicAzwAVAJXqeoUEflCvH4pcC9wJDAVWAmc0Vi6m8JAfVnjQTokXu7Nh82xzODl3tzYLMqtqvcSBmPrZ419KfDl5qQpWeYIHcdxHMcpPf6N2nEcx3HKmLIdqBszw9YREZHhIvKoiLwhIlNE5Kullqk9EZFKEXlJRO4utSzthYj0FpFbReTN2O57lVqm9kBEvh77+GsicqOI1DQea9NDRK4Skbki8prx6ysiD4nIO/F/n4bS2BTJKPevYj+fLCK3i0jvEoq4SVGWA7Uxw3YEMA44SUTGlVaqdmE98E1V3Q6YCHx5Myl3wleBN0otRDvzO+B+Vd0W2InNoPwiMhQ4F9hNVccTlG5OLK1UbcY1QPEWoPOBh1V1a+Dh+LujcQ31y/0QMF5VdyScmvid9hZqU6UsB2qaZoatw6Gqs1T1xeheRnhoN2ixpqMgIsOAjwFXlFqW9kJEegL7AVcCqOpaVV1cUqHajyqgi4hUAV1pZB/ppoqqPgEsLPKeBFwb3dcCx7anTO1BWrlV9UFVTc5xfYawf9hpAuU6UDfbxFpHQ0RGAbsAz5ZYlPbiEuA8oK6RcB2J0cA84Oq45H+FiHRrLNKmjqp+CPwamA7MIuwjfbC0UrUrA5N9s/H/FiWWpxScCdxXaiE2Fcp1oG62ibWOhIh0B24DvqaqS0stT1sjIkcBc1V1czsKtQqYAPxFVXcBVtAxl0ELiN9kJwFbAkOAbiJySmmlctoLEfke4TPfDaWWZVOhXAfqZptY6yiISDVhkL5BVf9ZannaiX2AY0RkGuEzx4Eicn1pRWoXZgIzVTVZNbmVMHB3dA4G3lfVeaq6DvgnsHeJZWpP5iSnJcX/c0ssT7shIqcBRwEnq+8NbjLlOlA3xQxbh0NEhPC98g1V/U2p5WkvVPU7qjpMVUcR2voRVe3wMyxVnQ3MEJFtotdBwOsNROkoTAcmikjX2OcPYjNQojPcBZwW3acBd5ZQlnZDRA4Hvg0co6orSy3PpkRZDtRR4SAxw/YG8A9VnVJaqdqFfYDPEGaUL8e/I0stlNOmfAW4QUQmAzsDPyutOG1PXEG4FXgReJXwHOqQVqtE5Ebgv8A2IjJTRD4LXAwcIiLvAIfE3x2KjHL/EegBPBSfbZc2mIiTwy2TOY7jOE4ZU5YzasdxHMdxAj5QO47jOE4Z4wO14ziO45QxPlA7juM4ThnjA7XjOI7jlDE+UDuO4zhOGeMDteM0gIj0M3vaZ4vIh9G9XET+3EZ5fk1ETm2LtFuCiEwTkf4NXL9JRLZuT5kcZ3PC91E7ThMRkQuB5ar66zbMo4pgCGSCOWmopETTrrup6vyM6/sDp6jqWe0qmONsJviM2nFagIgcICJ3R/eFInKtiDwYZ5+fEJFfisirInJ/tN+OiOwqIo+LyAsi8kBi77mIA4EXk0FaRM4VkddFZLKI3BT9uonIVSLyXDx1a1L0rxSRX8d8J4vIV6L/QTHcqzFe5+g/TUQuEpEX47Vto3+/WJaXROSvxENyYr73iMgrIvKaiJwQZX4SODi+ZDiO08r4QO04rcMYwnnak4DrgUdVdQdgFfCxOFj/Afikqu4KXAX8NCWdfQB7itj5wC6quiPwhej3PYI99N2BjwK/isdjnk04kSoJf4OI1ADXACdEeaqAL5r056vqBOAvwLei3wXAU/FEr7uAEdH/cKBWVXdS1fHA/QCqWgdMBXZqToU5jtM0fKB2nNbhvngS1KtAJXEQi79HAdsA44l2joHvE06FK2Yw4YzqhMmEAfcUwtGAAIcC58d0HgNqCIPpwcClyWxcVRfGfN9X1bdj3GuB/Uz6yQltL0Q5idevj2ncAywyZTlYRH4hIh9R1SUmnbmEIysdx2llfKnKcVqHNRBmlyKyzhzhV0e4zwSYoqp7NZLOKsLAm/AxwsB5DPADEdk+pnWcqr5lI8aTqIqVTtLOdq8nN7CBwudBPeUVVX1bRHYFjgR+LiIPquqP4uWaKLvjOK2Mz6gdp314CxggIntBOHc8DrrFvAFsFcNUAMNV9VHgPKA30J1wqtxX4sCMiOwS4z4IfCH5ViwifYE3gVEislUM8xng8UZkfQI4OaZxBNAnuocAK1X1euDXFJ6dPRbYHE64c5x2xwdqx2kHVHUt8EngFyLyCvAysHdK0PvIL01XAteLyKvAS8BvVXUx8GOgGpgsIq/F3wBXEM56nhzz+LSqrgbOAG6J6dQBjR0veBGwn4i8SFhmnx79dwD+F5fcvwf8BEBEBgKrVHVW02rDcZzm4NuzHKfMEJHbgfNU9Z1Sy9IUROTrwFJVvbLUsjhOR8Rn1I5TfpxPUCrbVFhMUFJzHKcN8Bm14ziO45QxPqN2HMdxnDLGB2rHcRzHKWN8oHYcx3GcMsYHasdxHMcpY3ygdhzHcZwyxgdqx3EcxyljfKB2HMdxnDLGB2rHcRzHKWN8oHYcx3GcMsYH6jZERB4Tkc+VWIZrROQnLYg3TUQObguZOhoi0llEXheRQfF3FxH5l4gsEZFbSi2f47Q3IvK/jNPhnBbgA3URdoASkdNF5Kmia6tEZLmIzI6DYPfSSeuUCWcDT6jq7Pj7k8BAoJ+qHr8xCYvIhSJy/cYK2Ja09GWwFfL9lIg8LSIrReSxlOs7i8gL8foLIrJzA2n1FZGbRWR+/LtBRHpmhD1ARGam+G/0i3l85qiI/L8i/5kicsDGpJ2SV2b9iUh/EfmPiCwQkcUi8l8R2cdc7ywivxWRWhFZJCJ/FpFqk8SvgR/htAo+UDefo1W1O7AzsAvwndKKU3qS84/LkXaS7fPA38zvkcDbqrq+HfJukI0tfzm3LbAQuAS4uPiCiHQC7gSuJ5ynfS1wZ/RP4ycx3GhgDOFF68JWl7hpLAS+nfWi0Mr5XEJK/QHLgTOBAYR6+QXwL9Mfzgd2A8YTziKfAHzfxL8L+KiIbEqHy5QtPlC3kDh7eoAwYDfEmLgMtERE7hSRvskFEbklzsyXiMgTdqlIRI6My6nLRORDEfmWuXaUiLwc33SfFpEdzbVdROTFGO9moKYh4UTkLBF5I4Z/XUQmmMs7i8jkKN/NIlIT4xwQ3/C/LSKzgavjG/Yl8Q27Nro7F4U/T0TmisgsETk2lvFtEVkoIt81Mu0R3+AXx7B/tA9YETlURN6Kcv1ZRB5PZjJxRvKf+La/ELhQRMaIyCNxdpDMlnqb9KaJyP+LZV0hIleKyEARuS/Wy79FpE9G/Y0gPNifjb8vAn4InCBh5eWz0f/MWM+LROQBERlp0vidiMwQkaUSZn4fif6HA981ab1i5D3YxM/NukVklIQZ2WdFZDrwSGP5F5UnK35qXxWRs4GTgfOijP+K/kNE5DYRmSci74vIuWn5bQyq+m9V/QdQm3L5AKAKuERV16jq7wEBDsxIbkvgDlVdqqpLgNuBFi/dSvj0sdz81YnI6U2M/gbwX+DrLc2/KTRUf6q6WlXfUtU6Qr1tIAzYyfPraOD3qrpQVecBvycM7Ln4wAuE88ydjcQH6hYiIsOAI4CpjQQ9ldCBhwDrCR064T5ga2AL4EXgBnPtSuDzqtqD8NaaPDAnAFcRZnH9gL8Cd8WBshNwB2F21xe4BTiugTIcT5g1nAr0BI4BFpggnwIOJzzEdgRON9cGxTxGEpZ+vwdMJLy47ATsQeEb9iDCS8NQwkB2OXAKsCvwEeCHIjI6ht1AeEj1B/YCDgK+FGXuD9xKWMnoB7wF7F1UtD2B9wj1+lPCg+bnhDbYDhhO/dnSccAhhNnB0YS2+W6UoQLIGmh2AN5LZs+qegHwM+BmVe2uqleKyLExrU8QZihPAjeaNJ6L9dYX+Dtwi4jUqOr9RWntlCFDGvvHsh7WhPwbjB9/p/ZVVb0sun8ZZTxaRCqAfwGvENr7IOBrInIYKYjI+fGlLPWvGWW2bA9M1sLjASeTPfj+CThKRPrEl7LjYplbhKoeHeujO+FTyGzg4WYk8QPg62Je7LNoo/pL0p4MrCbMkK9Q1bnJpfiH+T1MRHoZvzcIzwJnY1FV/zN/wDTg4Og+HXiq6NpyYBmghBuvdwNpPQZcbH6PA9YClSlhe8c0e8Xf0wmDcc+icH8Bflzk9xbhwbof4e1YzLWngZ9kyPcA8NUG6uEU8/uXwKXRfUAsR425/i5wpPl9GDDNhF+VlBvoEcu6pwn/AnBshixfA26P7lOB/5prAswAPmfabHojbXws8FJRWU82v28D/mJ+f4Uw20pL62TgmSK/C4Hrze/7gM+a3xXASmBkRpqLgJ3S0iruo8VhgFGxbke3JP+0+E3oq9fYPkZ4UZpeFOc7wNWN3X8t+QM+BzxW5PcD4KYivxuACzPSGAL8G6iLfw8BnTLCHhDDLC76W5/0QxN2LDAX+EgTy3I68ZkD/AP4RXTPBA5or/orul4DnAScZvx+AvyH8OI3iLCipMBgE+anwFVtIfPm9ucz6uZzrIZZ7gHAtoQZFyJyqVnm+q4JP8O4PwCqgf4iUikiF4vIuyKylPDwJUmP8EZ/JPBBXNrdK/qPBL5Z9MY8nPCgGQJ8qPEuMXlmMZwwwGYx27hXAlZxbp6G5a2EIUV5fRD9Ehao6oboXhX/zzHXVyXpi8hYEbk7LrUuJcwqk3oZgqnTWNZixR5b54jIFiJyk4RPCEsJ3y37F8UpliVVthQWEV48GmIk8DvTXgsJLxhDo3zfjMvSS+L1XinyNRdbBw3m31j8JvTVYkYCQ4r66HcJ333bi+WEVSJLT8JLdhq3AG8T2rIn4b5oSImvVlV72z/gKRsgzi7vBH6gqk82vwj8EPiixN0EpULDMviNwPkiksyQfwq8BLxMmAzcAawjvJQk9CC8wDgbiQ/ULURVHyfMJH4df39B41KXqv7MBB1u3CMInXk+8GlgEnAw4cE8KoaRmN5zqjqJsNR4B+HtGsID9KdFD4mu8UaaBQwVEbskNaKBYswgfF9tCVr0u5bwgLb5pn07bAp/Ad4EtlbVnoSHfFKmWcCwJGAs67Ci+MWy/Tz67RjTO4XCZbuNYTIwWhpWuppB+Ixh26yLqj4dv0d/m/CZoU984C8x8hWXBWAF0NX8TnuQ23iZ+Tcgs43fYF9NkXEG8H5Rfj1U9ci0jETku0Xfcwv+GpCxIaYAOxbdCztG/zR2Av6qqitUdTlwKeFFuUXE5f+/A4+q6l9bkoaqvgn8k9D/G8qrLeovjWqCsh2qukpVz1HVoao6mvDJ7AXzMg7h08krrZj/ZosP1BvHJcAh0sC2D+AUERknIl0J2xVujZ25B7CG0MG7EmaNQNBYFZGTRaSXqq4DlhK+20L4tvsFEdlTAt1E5GMi0oOggLIeOFdEqkTkE4RvxVlcAXxLRHaNaW0lGUpGTeBG4PsiMiB+R/4hDc9IGqIHoczLRWRb4Ivm2j3ADhKU0aqAL5M+UBWntxxYLCJDgf/XSPgmo6ozgXdouJ4vBb4jeQWsXhL0AxLZ1gPzgCoR+SGFM8E5wKj44E94GThRRKpFZDfCN9CGaCj/ppDZV42Mo83v/wFLJSgbdokz8vEisnta4qr6M/OSW+8vS6iYbg1BaaxCRGokv0XoMcI9c64E/Y1zov8jGck9B3wuytuFoHexMYPMT4FuwFdT5H5MRC5sYjoXAWcQPjek0hb1JyITRWTf+CzqIiLfJqyIJEqTQyUoDIqITCR8arjApN2ZoH/yUBPL6TSAD9QbgQZtx+sInTSLvxFm3rMJ33oSpaTrCMvDHwKvA88UxfsMMC0uNX6BMAtEVZ8HzgL+SFh2nUpU8lLVtQSFodPjtRMIb+RZ8t9CeKD8nbAkeAd5rc7m8hPgecIM81WCwlFL99Z+izCLW0Z4MbnZyDwfOJ7wzXwB4bv/84SBJIuLCNtHlhAG+sw6aSF/JbRXKqp6O2F7y02xPV8jKCJC0BO4j7Ds+gFBcccuWycGUxaIyIvR/QPCSsgiQtn+3pBwjeTfFBrrq1cC4+Iy9x3xRfRogoLc+4QVpCsIs/HW5DOEzxJ/ISgkriL0l+ReOJag07CYoNB5bPQnvgjb2fWZhJWCmYRyjqZQebK5nERQrlxkZrcnx2vDCd93G0VV3yc8Q7pthCxZZNYf0JmgYLeAUB9HAh9T1WSVbAxhyXsFYevb+ar6oEn7GMJ375auqjkGKfyc6TibFnGmOZOgDPZoiWToTPhed5CqziqFDM6mgYTdIreo6l6NBt6EEZFnCQqMr5Valo6AD9TOJoeEbT7PEmYA/4+w/D1aVVc1GNFxHGcTxJe+nU2RvQhaufMJS6zH+iDtOE6pEZGrJBh1Sl1JiN/0fy8iUyUYWJqQFq5ePJ9RO47jOM7GIyL7ERRXr1PV8SnXjyTYZTiSYG/gd6q6Z2Pp+ozacRzHcVoBVX2CYKcgi0mEQVxV9RmgtzTBHnq7GdwXkQ0EbeDEbuw5jezjpGfPnjqwfzCxLBX5d4qqqrzYaZth7RqBvW4XD9SE2rDenJ1gtl0mWzAr7FZM685ajSgIk3euW78u711XF/Kuq2NdLFqd1hm5jRwV6enZ1ZBE1oKwBq1rfOWkYMuppPibJCoq8+1REXcOVa+DCtNO1m1lTcoeE6+Xd529nlLGkH9lqv9625Yp1wvqrDBQQfh1VNcrQ66OpSItWgE2nw0b8ltLJaV/2TQrKrKup5fB1tW6dfn+ZfPsVBnqpKo6/XZP60dBFtN+pv/YfJJOYevElsHKl5VP4c6zmEfKvQKwRkzZ1faTvFOyGiXletaKYpZ/Vtpp4aur84dJ2bqv1vwW+ErTj5uSf0JWu9teXdiWxdY+i9vAXM0oo80nka+6cl3hPR/bUlO3/xc91wpvwJyrrs5sxTaBKiuSe8TUWcHjOOQ548PZLFy4sEl2EkSkuUvKUwg7MxIu02BGt6kMpXBXx8zo16ASanuejLNKVXeGnDLQzwlmLzMZMGAAv/nxeQDU1NQU+CekdSrbiW0nsg9xG2bhovwLUIV5aCQ3WufOnXN+9iUhbVAoDmPzmTMnb+xq9erQ1itWrKA22rVatSr/mdWm0alT/sAfm57NPwlvZbU3u73JbBq2/uxDw+afuG163brld4skeQ6pha5d8w8h67ayJmW3+duH2sqVK1PLYMN0757fHlpt6mfB/Pn14ma1R1bZq6urqY1G1Wy/S9rB+mW9mNj6Xrx4cc5tZbHlSdK0bW3b0spny2Dr6sMPP8y5lyxZknOP6BOMRQ3YYgvSsC+qVqYuXbrk3GvXrs25Z83KP1MSWaystn6sfLZObD42bi6P2fk81q7J5/1uVf4esTLZF5OCF/mUQdnmbevSppH1ctVYXBtv8OD8RGngwLxRtkF1+c+Sth/bfGw6aX5r1uR3I9r2yJK1piZfx8lAZ6/bvmv7oGX27LyhwqQth/SoLbjPk7a3staZe7gq4xljJxgrlq/Iy2XC94h1ZZ89ac/3Q487PVX+LBp7sbNEK227NSuDouzSkm0sUqmOsOtJ2APqOI7jOCVBRApeGBqjcPWiRcyk0FrlMJpgwbE9B+ouIvIywejHYLKPm3Mcx3GcdiHr80MarTBQ3wWcIyI3EZTJljTF9kKplr73Aq4TkfFa9DFGwvm2ZwP077+x5xI4juM4TjrNnVE3Ib0bCQc29ReRmQSzqtUAqnopcC9B43sq4aCjM5qSbkmWvlX1v9Ee9AAKT1shfpi/DGDMmDG+d8xxHMdpE0SkWTPqxlDVkxq5rgQDTc2iJAN1PGihkmBHNpMuXbqw3bjtAKjbkFfcKNAKbsAPspV3rLKDVZqxigWdowJG507pymRZSmu24Wtr858fRozIH2SVKND07Nkzp0xmlXAsy5enH4BjwycKWjaslalnz/w5D1nKInZxI+0t0yoGpWkkD+lbpORl6tsqkFkloEQBxMr6wQf50zKt4kia8lWxu5cpZ9p1i13GsspuIkJdPOfDKjolMmYpHWVp6tr+ZUlThMnUUM9QNLLuvn3zZtr79OmTcw/rVVMvvYLyZshksfUwdGj90zGtQlGW8mWWIuTChXllzmXLwimUo0aOyvnZvrvDqH45t+0PNj1bTnsvJmVbsSKvrGTrz5bdpm3vF6vEZcuTlNOmt2hRXg3HylexqHEF16QMWVrzNp8tjJKg7SdW7rTdC2kKo1BYZzYf+/xK5B7SI19PVkZbT/Y+y1Jas32mS03+frFlTsLb+ymtrbP6cBatOaNuK0rxjRrCs+G0oiPRHMdxHKfdaO0ZdVvRbgO1qpZ/bTiO4zibDa39jbqtKH8JHcdxHKeN8Bm14ziO45QpPqN2HMdxnDLGv1G3Mlbz02oWJ5WcpkkMRfZ2zZuT1UK2WqdWWzEx7GbTKLBZnaHlaxt++PC8ERpr4nHp0qVAMPWYaH1bbcYsO9lZGpmJtrc1V2k1Rucb05pZpinTNEMhXydWi9XWX86cYG2h1rkNYzVt0/xtmw0aNCjntuZibXmzDA/06NEj5040xrPMRGaVF6CWYC4xrb6b0r+ywmRp+aZh5Uvr82lyJ/Tq1SsfpncIYzXos+yFW/mytMtt+LT7z5qDtP4WK7eVNbnPF5q+Zs2hLpqVT89q81vzu7Zs9p5KymbLYrXVs2zG290OxbsDEpJy2ufH9OnTc+7kfgdYOfWDevEg3Xxrlta87QPDhg1LTSPrWZHUj70nrRxpOx2sTJBvs1VVqwp2GCRxs8zpWrK00u3zwdZ3Uh5bxrTdEM0xCeozasdxHMcpc3xG7TiO4zhlii99O47jOE4Z40vfjuM4jlPm+IzacRzHccoUn1E7juM4Thnj36hbgQ0bNrBkcdiaYQ/lSDPUnmWM3r4tZW2nytpCkpxWYLcxFGwpqEzf3mO3FCSHDBT7J1sMampqWB53FNltKk052CDtgICsQyLsNpSs7WYW23mTbTD22FG7HSZhSK/CbXRZ20OsO6k3G3bWrPzxrLbsWQb9s7Y/JfnYtK3bbgOx7VddXU1PhgCFW5qSMDZs1qEJFtsHrKy2TZJy2uuWrK051m235tk8k3LabTJV1em3vt2C01nyfdDWlSXJ37aH1qUf/mHbz7ar3f6U9Csbz94LdvudlbVnymEsVj7rttu9surVlietvxaXoXfv3vWu275TsC1v4KDUMGnbirIGEduWixfbwz/ystq+ZLf3JXLbraM2n6xnpq23xL+ysrLgOZS0cVZ6WQefFByGZNo77VmeVSdp92dT8Bm14ziO45QpPqN2HMdxnDKmQ36jFpGJwB+A7YBOhDOlV6hq+rqT4ziO45QxHXFG/UfgROAWYDfgVGCr1hbKcRzHcdqaDjmjBlDVqSJSqaobgKtF5Ok2kMtxHMdx2pSO+o16pYh0Al4WkV8Cs4BujcRBRAYCvwUmAouAtcAvVfX2huKtXr2aqVOnAoWa2VYLMyFNE7w4XtbhFlnuRLMyS5Paahdat9WItlqqVssx0XRdtmxZ7lAOq11utSOzDNxbjdVEq9MayLfxrExWy9aWzdaVDZ+E6devX85v5MiROXeiFb6utlCT2WpvJlqxxSRtZbVibR3bQz5seSxWw3nevHk5d5qmsm1fqwlr26mmpoba6LZv20k5s/qAxcqadfiGreOkb9j0bD62LFkHalit75kzZ+bcU6verRc260CSLE1cG97WYdKXbN+1fSrrABHb3lZ7O8nfXl+8JF+uyavzh8vY/mrb0pYh7VmRqY2d0U5WqzlL7jTGjh2bc2+55ZY5d9+1+Xs0a9dAWn/Iku/996fl3Pbets+KNI32gQMH5vxsnWS1n9X6njNnDgBDetSyYMGCevkX7B7IOATIljfrgJC0fpqlFZ7Emzt3Lk1lU5lRpz/5svkM4bv0OcAKYDhwXEMRJNTuHcATqjpaVXclLJ8Payie4ziO47Q1lZWVTf4rFc16lVDV5Hy2VcBFTYx2ILBWVS8tSucPzcnbcRzHcVqTTWVG3Vyt76OAHwMjY1wBtBGt7+2BF5uRx9nA2VC41Oo4juM4rUlH/UZ9CfAJ4FXNMsPUCCLyJ2Bfwix79+LrqnoZcBnAlltu2aI8HMdxHKcxOuSMGpgBvNbMQXoK5ju2qn5ZRPoDzzczb8dxHMdpVTrijPo84F4ReRzIqUKq6m8aiPMI8DMR+aKq/iX6dW0gfI7OnTvntCWtJqd9A0o0AK0moNVatP5WE9FqUy5YmNdatHaKkzyzNF6tdqLVDF26dGnObTVxreZuogXdqVMn+kWFY6u5mqX5mGgeQ6EWaKLhaeNl2TG2mqvWbUlL29ar1S5PbJQPqSi0B27rO0uLPU2T27aNLY8tg21Xi9V6TcpgNdHTNJZtWAjtXhltfVv760kYW2dWVpu27Q9WCzVLY9xqrybYfm7LkGWb3qbdt2/fnHvrQUFD37ZHlvZtVhmsZq+tq0RuqzWcVQZbXntfLFy4sF6eNu8RI0bk3OO3TreTbcuQZVs6SdNqL1vNaPuMSWsPKKyfNI1kez1Nqx+gU12+71ob5bZek2eF9cvSmh86dGiqTPZ5kvbsW7jQtqmmxst69myxxRYADOnRr6C+k7hZGvFZc7zGngmQr9usswqSOrn8pntS46fRUWfUPwWWAzUEy2SNoqoqIscCvxWR84B5BI3xbzczb8dxHMdpNTrqN+q+qnpoczNR1VmELVmO4ziOUzZsCjPq5u6j/reINHugdhzHcZxyI5lRt+Y+ahE5XETeEpGpInJ+yvUDRGSJiLwc/37YWJrNfZX4MnCeiKwFko87jW3PchzHcZyyo7WXvkWkEvgTcAgwE3hORO5S1deLgj6pqkc1Nd3mGjzp0Xgox3Ecx9k0aOWl7z2Aqar6HoCI3ARMAooH6mbRbAlF5Bhgv/jzMVW9e2MEaAhVzWkaWy1fq32YZhtYyWsCWg1Cqy1rbUhbjVZLosFoNW6tlqHVgraaiH375TVurY1rq22apNOpUydWxdcf22GsRqvVTLVlsFrkidvKYctu/W15bBmsVqnNP9E8tfJZrdgkn14rCrU9bTvZuGn2xbPqMssmddbNZfvJiqjVbdva1t/8+Xm70VbzePXq1dRGrW+ruZvUQ5aGqs07S4PfltNqnSf9tyn247M04a2m9/Dhw3Pu3pWLgfQ2g8L6tm2WZX/d9sdExiz70Fk7DGw+o0ePzrmTOq5To+1Mvh5m9Uy3P27d9n62siZ52j6aZSM8TZu4mDTteyvHlClTcu7Zs2fn3EvffCrnts+ENJviWfbWrXv8+PE5d5Yd8zQ74nZ3iu1fVg7blommN8DgwYMBWNajtsA/ScfuCsnSlLd9IGu3gS1DEsY+v9J2A2XZ30+jBTPq/iJitxZfFm1/JAwlbGNOmAnsmZLOXiLyClALfEtVp6SEydFcy2QXA7sDN0Svr4rIvqpabx3ecRzHccqZFmzPmq+quzWUZIpf8Z60F4GRqrpcRI4knIWxdUOZNndGfSSws2p43RWRa4GXAB+oHcdxnE2OVt6eNZNwWFXCMMgdxgeAqi417ntF5M8i0l9V55NBSxbnewPJOmGvBsI5juM4TtnSBgZPngO2FpEtgQ8J25I/XZTnIGBOtDGyB2H3Vfr310hzJfwZ8JKIPEqY4u8HfKeZaTiO4zhOyWltrW9VXS8i5wAPEI6EvkpVp4jIF+L1S4FPAl8UkfWEkyhPbMwsd5MHahGpAOqAiYTv1AJ8W1VnNxjRcRzHccqQtjAhqqr3AvcW+dljnv8I/LE5aTZZQlWtE5FzVPUfwF3NyQRARDYArxqvm1T14obirF69mqlTpwKF2n/W/m2ibZql4ZhliznLHrB1J/lY7WqrkWixaVtNUqsJPHDgwJy7R4+g6t2pUycSZVxrV9pi87flsRqPiVxpWpJQqCmZlU+WZm+iyWk1o5988smcO9EeHVILy5Yty/lbTeAsbc+kfux1q0Vq82yKjXKrZZ9oOdv+YsvYFPvCto7T7Kln1ZnV6B4wYEDOnbR7sX8S16aR1Y9t37X1YOWy9TZzXfhEltV3bRq2r2XZvrb5J33MpmHLkHVfZvWHRGt52LBhOT/bX7t0ybuzdiFk2c9OZEnTgC6WKcv+edZzKJHFtsGiRYtybmsDv2qLvO1yq6lvy5Dkn9W+9t564403cm5b91na00nadmeA1YTPejbaekju8w0bNvD+++/XC2/zthrlWf3EhrHtlzaIWlltPsk9ZzXsm0JHNCH6kIh8C7iZYK8bAFVdmB0lxypV3bmZ+TmO4zhOm9BRD+U4M/7/svFTYHRKWMdxHMcpWzrkoRyquuVG5NVFRF42v3+uqjdvRHqO4ziOs1F0xBk1IrI3MMrGVdXrmhC1SUvfInI2cDYUfr9xHMdxnNakQ86oReRvwBjgZSDRZlCgKQN1k4jm2C4DGDlyZIMq647jOI7TUjrqN+rdgHGN7flqLbp3787uu+8OZGudJtqPVgMzS9M7y86y1XRN097u2i2vMW3RunTNUIvN02riJlqOq1evZnVUPM2yxZyl6W39kyaxtoNtndi0rWa21UrP0nxOymBtLu+000715BhSW6jJbPO0NNZ9bBmsNqgtr9Vit1qgtl2TfGyd2bBWjmJN7sTWd1pfSrNVDoX9yIbJ0iBOa8ss++hZGuAWq0Vr23JYr9n10rPY9LK06S22nEmatl5tGW0ftFhZbJjEvSDlXgnufJ+y9bdixYpU/zQNdSv/3Llzc25bZ1bz2PY1295p7WDb3WpV27ap6prX+rayZmlEp+Vnr0+YMCG1DDbtNO33tLqBwj6Ydc5BksaQHoNTbcVn9f+s55p1W03utN0BWXInYW+461GaSoecUQOvAYOAWS3Iq/gb9f1uI9xxHMcpJR1mRi0i/yIscfcAXheR/wG51x5VPaaxNFS1/F9bHMdxnM2GjjajvgsYCDxZ5L8/wZ6p4ziO42xSdLRv1JOA76rqZOspIiuAC4ArW1swx3Ecx2lrOtKMelTxIA2gqs+LyKjWFclxHMdx2p6ONqOuaeBalwauOY7jOE5Z0tG+UT8nImep6uXWU0Q+C7zQ+mIF1q9fnzNsb9X37VaDRCU/69CCrO1ZdntDltH9+fPDOd7rZuXDZm0psFsQ7JYnG2b69Ok59/Lly4GwVao2ntOwePHi3PWsrT5Z23uSrVD2AAMbz255sltMsrZLpG3XsNs2ttpqq5w7OWykqqpwm0xSxnCtKtWd1JvN28pky5PVxrY/2K088+bNA7K339n6Sw6DgLAlZSlha57ddpSEse1r68S6rRzJwTJQWD9ph1dktYElq9/ZMtjtRSu7zwx5rEvf/mO34Ngy2G0yjW2py9pKZtPIOrgmrS0XLMgfz2u3NU5+fn5qGWx9W/+srWwJtp6yDlWx/STtkBYrt92SuM022+TcY8aMybm7ZfSftK1Glqwtem+//XbObbdeZh0gktyXdmuafVZkue2BN4l7SM9aevXM36NJu2bd77b/2zZL2+IFhfd/Un7bNjbtJG97vzeFjjRQfw24XUROJj8w7wZ0Aj7eBnI5juM4TpvSoZa+VXUOsLeIfBQYH73vUdVH2kwyx3Ecx2lDOtrSNwCq+ijQdLMvjuM4jlOmdKgZteM4juN0RDrcjNpxHMdxOgo+o24FKioqclq3VvvPaowmWpZZGo5Wm9C+OVntQ6tVaskdymG0Yq0WZNZBIFbrcMaMGTn3llvmj/NOtE379u2b0/q2mp5Z2tjWP+2QEatpbeNZ7damaBOnyWLLZTXU16wOmr1V1YXtYLVUszSzE3eWZnuWNnHW4RFW63bw4MH18s46IMO6O3XqRA9Co9i2TytL1sEnxVrkCVkaqUk/zTrAIKtOrH9WfY/o07/BsFlktWWyE8OSpQmfdf9Zjel33323Xp5DhgzJ+W233XY5926j+qXmafuPJa3Mtv/ZHRC2Xm2b2XawGseWpO1tuexOC3tv9ajOP8vS+hfk2y/rOWDLazXKrb/dsWDLlshow9rrNp5l6dKlOXdSl0N61BY8WxIZ0w4BKc7H9pOsHSo2naS9sw4SSmSy6TZGh/xG7TiO4zgdCZ9RO47jOE6Z4jNqx3Ecxylj/Bu14ziO45Q5PqN2HMdxnDLFZ9RFiMgg4BJgd2ANMA34mqq+nRVn6dKlPPbYY0ChlrHVOk00GK12aZZNb+tvNQNHjhyZc1sNxUTL0WqGWs3LrDytpqLVVh87dmzO3adPHyBonCda31Z70mpeZmmDW5JyWq3UCsmHzdLKzdL+tfkkaVs7wq+99lrOndTTkNpCzVBb91m2f5NyZtlkzmrLLO3pHj165NyJRm2W1rel2M5zLUHr2O4OSPqGbXeLtWtt07MazFmayolctq9Z7WRLlga41TS39dO7cmquXGlYOWw8W2+2zwwYMKBeXFtPto6tJrxtY3uf2TpJ6jaxHw8waNCgfLyafL1a29NZbZJ2v9q8bX3berDa2Fk2ti1p9+WsWbNy7pkzZ+bcK955K+e29Z2lIZ+Wh61XW1dZg05aGbLssFuZbLta2+CJ/5AetSxZmtfSXrZ0WYNp2zJkaZrbfmf7bOK2MqX1UbuLoDH8G7VBQs+4HbhWVU+MfjsDA4HMgdpxHMdx2gqfURfyUWCdql6aeKjqy+2Ut+M4juOksinMqNPXUVuf8TTxOEwROVtEnheR5+0Sh+M4juO0JsmMuql/TUzzcBF5S0Smisj5KddFRH4fr08WkQmNpVl2c35VvQy4DGDo0KGNm09yHMdxnBbQ2t+oRaQS+BNwCDATeE5E7lLV102wI4Ct49+ewF/i/0zaa6CeAnyynfJyHMdxnEZpg2/UewBTVfW9mP5NwCTADtSTgOs0aPg9IyK9RWSwqs6qn1ygvQbqR4CfichZqno5gIjsDnRV1cezItXW1s7/+g9/tQKY305ylhP98XJvLmyOZQYv9+ZGe5V7ZONBAi+88MIDVVVV/ZuRdo2IPG9+XxZXgROGAjPM75nUny2nhRkKlHagVlUVkY8Dl8Q1+9XE7VmNxBsgIs+r6m5tL2V54eXefNgcywxe7lLL0d6UY7lV9fBWTjJt/17xJ9ymhCmg3b5Rq2ot8Kn2ys9xHMdx2pmZwHDzexhQ24IwBbSX1rfjOI7jdHSeA7YWkS1FpBNwInBXUZi7gFOj9vdEYElD36ehDLW+U7is8SAdEi/35sPmWGbwcm9udPhyq+p6ETkHeACoBK5S1Ski8oV4/VLgXuBIYCqwEjijsXSlKQfIO47jOI5TGnzp23Ecx3HKGB+oHcdxHKeMKduBujEzbB0RERkuIo+KyBsiMkVEvlpqmdoTEakUkZdE5O5Sy9JeRGMHt4rIm7Hd9yq1TO2BiHw99vHXRORGEalpPNamh4hcJSJzReQ149dXRB4SkXfi/z6llLEtyCj3r2I/nywit4tI7xKKuElRlgO1McN2BDAOOElExpVWqnZhPfBNVd0OmAh8eTMpd8JXgTdKLUQ78zvgflXdFtiJzaD8IjIUOBfYTVXHE5RuTiytVG3GNUDxXt3zgYdVdWvg4fi7o3EN9cv9EDBeVXcknJr4nfYWalOlLAdqjBk2VV0LJGbYOjSqOktVX4zuZYSH9tDSStU+iMgw4GPAFaWWpb0QkZ7AfsCVAKq6VlUXl1So9qMK6CIiVUBXGtlHuqmiqk8AC4u8JwHXRve1wLHtKVN7kFZuVX1QVZPDwJ8h7B92mkC5DtRZJtY2G0RkFLAL8GyJRWkvLgHOA+oaCdeRGA3MA66OS/5XiEi3UgvV1qjqh8CvgekEs4lLVPXB0krVrgxM9s3G/1uUWJ5ScCZwX6mF2FQo14G62SbWOhIi0h24Dfiaqi4ttTxtjYgcBcxV1SYdhdqBqAImAH9R1V2AFXTMZdAC4jfZScCWwBCgm4icUlqpnPZCRL5H+Mx3Q6ll2VQo14G62SbWOgoiUk0YpG9Q1X+WWp52Yh/gGBGZRvjMcaCIXF9akdqFmcBMVU1WTW4lDNwdnYOB91V1nqquA/4J7F1imdqTOSIyGCD+n1tiedoNETkNOAo4Wd2IR5Mp14G6KWbYOhwiIoTvlW+o6m9KLU97oarfUdVhqjqK0NaPqGqHn2Gp6mxghohsE70OovA4vI7KdGCiiHSNff4gNgMlOsNdwGnRfRpwZwllaTdE5HDg28Axqrqy1PJsSpTlQB0VDhIzbG8A/1DVKaWVql3YB/gMYUb5cvw7stRCOW3KV4AbRGQysDPws9KK0/bEFYRbgReBVwnPoQ5pXlJEbgT+C2wjIjNF5LPAxcAhIvIOcEj83aHIKPcfgR7AQ/HZdmlJhdyEcBOijuM4jlPGlOWM2nEcx3GcgA/UjuM4jlPG+EDtOI7jOGWMD9SO4ziOU8b4QO04juM4ZYwP1I7TACLSz2yVmy0iH0b3chH5cxvl+TURObUt0m4JIjJNRPo3cP0mEdm6PWVynM0J357lOE1ERC4Elqvqr9swjyrC/uIJ5gCDkhItxu2mqvMzru8PnKKqZ7WrYI6zmeAzasdpASJyQHJutohcKCLXisiDcfb5CRH5pYi8KiL3R7OwiMiuIvK4iLwgIg8kZiSLOBB4MRmkReRcEXk9nuF7U/TrFs/7fS4e5jEp+leKyK9jvpNF5CvR/6AY7tUYr3P0nyYiF4nIi/HattG/XyzLSyLyV6Lt/ZjvPSLyioRzpE+IMj8JHBxfMhzHaWV8oHac1mEM4ZjOScD1wKOqugOwCvhYHKz/AHxSVXcFrgJ+mpLOPoA9nOR8YJd4hu8Xot/3CGZWdwc+Cvwqnrp1NuGgiyT8DSJSQzgb+IQoTxXwRZP+fFWdAPwF+Fb0uwB4Kh4UchcwIvofDtSq6k7xHOn7AVS1DphKOE/bcZxWxgdqx2kd7osHTLwKVBIHsfh7FLANMJ5oPhH4Punn8Q4mHH2ZMJkw4J5COHEI4FDg/JjOY0ANYTA9GLg0mY2r6sKY7/uq+naMey3hDOyE5OCXF6KcxOvXxzTuARaZshwsIr8QkY+o6hKTzlzCSViO47QyvlTlOK3DGgizSxFZZ04GqiPcZwJMUdW9GklnFWHgTfgYYeA8BviBiGwf0zpOVd+yEeMBF8VKJ2lHxtaTG9hA4fOgnvKKqr4tIrsCRwI/F5EHVfVH8XJNlN1xnFbGZ9SO0z68BQwQkb0gHGcaB91i3gC2imEqgOGq+ihwHtAb6E44rOYrcWBGRHaJcR8EvpB8KxaRvsCbwCgR2SqG+QzweCOyPgGcHNM4AugT3UOAlap6PfBrCo/kHAtsDgfnOE674wO147QDqroW+CTwCxF5BXiZ9DOY7yO/NF0JXC8irwIvAb9V1cXAj4FqYLKIvBZ/A1xBOEJycszj06q6GjgDuCWmUwc0dmrRRcB+IvIiYZl9evTfAfhfXHL/HvATABEZCKxS1VlNqw3HcZqDb89ynDJDRG4HzlPVd0otS1MQka8DS1X1ylLL4jgdEZ9RO075cT5BqWxTYTFBSc1xnDbAZ9SO4ziOU8b4jNpxHMdxyhgfqB3HcRynjPGB2nEcx3HKGB+oHcdxHKeM8YHacRzHccoYH6gdx3Ecp4zxgdpxHMdxyhgfqB3HcRynjPGB2nEcx3HKGB+oHcdxHKeM8YG6HRGRC0Xk+hLLcLqIPNWCeNeIyE/aQqaOiIjcKCLHmt8/EZH5IjK7hGI5TkkQkd+IyBdKLcemymY/UIvIp0XkeRFZLiKzROQ+EdnXXB8nIneJyBIRWSYij4jIxHjtIzHechFZISJqfi8XkRGlK5lTKkRkR2An4M74ezjwTWCcqg7ayLQPEJGZGy9l29HSl8FWyHe8iDwQX4jqHWIgIn1F5PZ4r34gIp9uIC2JL1cfxnv/sYzzw5Pwas78Tvw2+sVcREbFtO8p8r9eRC7cmLRT8mqs/q6Pz8ilIvK2iHyu6PrnRGRqfPbdH88vT/gV8D0R6dSaMm8ubNYDtYh8A7gE+BkwEBgB/BmYFK+PAf4DvApsCQwB7gAeEpE9VPVJVe2uqt2B5Cbunfip6nQ2Q0SkqtQyZNFOsn0euEHzJ96MBBao6tx2yLtBNrb85dy2wDrgH8BnM67/CVhLuNdPBv7SwOB7PHAm8BGgL/Bf4G+tKm3zmCgi+7RxHo3V38+BUaraEzgG+ImI7AogIvsTnqOTCPX1PnBjEjGeVf5mjOc0F1XdLP+AXsBy4PgGwvwNuDfF/y/Ao0V+owAFqhpI70LgVuBmYBnwIrCTuX4+8G689jrwcXNtK+BxYAkwH7jZXNsWeAhYCLwFfMpc6wfcBSwF/gf8GHiqARn3BZ4mHF04Azg9+l9DeNDdE+V7Fhhj4inwZeAd4P3odxYwNcp1FzCkKPyXYvhlUa4xhAfiUsIDo1MM2we4G5gHLIruYSatLYEnYjr/jnJeX9QunwWmA09E/1uA2bE+nwC2N+ldQ3hhuy/2kf8AgwgvdYsID5xdGqjD94B9o/tgYBVQF9O6JvpPNPX8CnCAiX8G8EYsz3vA56N/t6K0lhNeHq8BfmLiHwDMNL+nAd8GJgNrgKqG8k8pT1r81L4KbAesBjZE+RZH/87Ar2MbzAEuBbq00b29FaBFft0Ig/TYovv74ow0vg38w/zeHljdQJ4KbJVyvyf98DzTZssJg+I1TSjLqJj2tzHPHOB64ML2qr+UMNsAs4jPmti2fzLXh0S57TPie8DVbSFzR/8ruQAlKzgcDqyn4YF1NnBGiv9HY9wa45fcUI0N1OuATwLVwLcIb57V8frxsYNXACcAK4DB8dqNsaNXADXkB4JuhAH1jPgAnUAYyLeP128iDHrdgPHAh2QM1IQVhWXASVG+fsDO8do1hAF3j5jPDcBNJq4SXhb6Al2AA6McEwgP6T8QB0kT/i6gZ3wIrgEeBkYTXqJeB06LYfsBxwFdgR6EQfYOk9Z/44OiE+FFYyn1B+rrYh10if5nxrQ6Ewbgl01610TZd411/Uhsp1OBSuAnFL2ombjdYn4DjN8BFA6cQ4EFwJGxPQ+JvwfE6x8jvLQIsD+wEpiQlpaRt7GB+mVgeGybBvNPKVNB/Cb01dMp6mOxju8i9I8ewL+An2fkty/hBSLrb99G7u20gXoXYFWR37eAf2WkMZLwIj2WcC/8EtPnUsI3OFAX+Q8HaoEjm/CcGhXT7k64dw+O/pkDdVvUn7n2Z0J/1Fg/3aP//wF/LurjCkwyfp8AXmyszP6XUu+lFqBkBQ9LX7MbCbMeODzFf9vYCe0MMbmhGhuonzG/KwhvpR/JCP9y0tEJA81lmJlk9D8BeLLI76/ABYRBZR2wrbn2M7IH6u8At2dcuwa4wvw+EnjT/FbgQPP7SuCX5nf3KMsoE34fc/0F4Nvm9/8Bl2TIsjOwKLpHxHbqaq5fT/2BenQD7dI7hullynq5uf4V4A3zewfiTDElreQBZV/iDqBw4Pw28LeieA8QX0xS0rwD+GpaWkbexgbqMzci/4L4Teirp9s+RnjhWEHh7Gov4spLa/+RPlB/hKL7nbDi81hGGp2A38W2XE94UduygTyV8IK42PytpmigJrwoFfT1RsqS9N8qwgrUM6aPX9he9Vd0vZLwMvB98pOMgwgvtzvGMv6VsPJzkol3CPBeW8jc0f8252/UC4D+jXxzmw8MTvEfTLh55mdFFJGTjVLZfebSjMShqnXATMLMBBE5VUReFpHFIrKYMAPuH4OfR3jg/U9EpojImdF/JLBnEifGO5mwVDuAcIPn8gQ+aKC8wwnLmVlYjeWVhMHXYvMZYvNS1eWEOh9qwswx7lUpv7sDiEhXEflrVABaSliq7i0ilTGfhaq6MkOOen4iUikiF4vIuzG9afFSfxO+SbKlsDj+75FxHUKbHV/UZvsS+5qIHCEiz4jIwnjtyCLZWoKtkwbzb0L8xvpqMQMIqyEvmPD3R//2Yjlh9cbSk7CClMYFwO6Ee6IGuAh4RES6NpDHBFXtnfwBF6eEuRJ4S1V/0RzhI5cDA0Xk6BbEbTVUdYOqPgUMA74Y/R4m1NlthPt+GqFureJjD/L3h9MMNueB+r+EN95jGwjzb8ISXzGfIrzZrs2KqKo3aF6p7AhzaXjiEJEKQmevFZGRhBvxHKBfvNFfIwzOqOpsVT1LVYcQlJX+HLVMZwCP2wdEzPOLhG+6622ehBloFjMIS64tRY27ljAgJGXtRljC/rAF6X6T8E1sTw2KLPslyRJWJPoWPUCHUx8r26cJSi8HE5bZR5n0NgpVXUF42RnbQLAZhBmtbbNuqnqxiHQmPOx+DQyM/eBeI5umpLeCMBAmpGmW23iZ+TdUtMTRWF9NkXE+4eVme5NfLw1KmPUo2k2R9veRBuTM4m2gSkS2Nn47AVMywu9E0AOZqarrVfUagq7EuBbkDYCInE/ox1nKWg2iqusILww/poG+2kb1l0YV5nmhqn9S1a1VdQtCH64i9IuE7Qj6EE4z2WwHalVdAvwQ+JOIHBtnbdVxNvPLGOwiYG8R+Wnc2tFDRL5C+B58QQuz3lVEPhFn8l8jfJt9hvy3zXkAInIGYZZC/H28iAyLPxfFsBsIilVjReQzUf5qEdldRLZT1Q3AP4ELY/nGAac1INsNwMEi8ikRqRKRfiKycwvL+XfgDBHZOQ4+PwOeVdVpLUirB+FBv1hE+mLqXlU/AJ4nlLGTiOwFNDbj6EGo9wWEAe5nLZCpIe4lfFvO4nrgaBE5LM7uayRsuxpGWHLtTHzJEpEjgENN3DlAPxHpZfxeBo6MfXQQoV81REP5N4UG+2qUcViyFSeuHF0O/FZEtohxhorIYWmJq9lNkfH3ZFo8CdQQ6pBYrs4xzRWEe+FHItJNggb1JLI1uZ8jrDoMFJEKEfkM4Vv11CbWUbFsRwDnAseq6qqiaxeKyGNNTOpvhP5xeFaAtqg/EdlCRE4Uke6xzxxG0GV5xIQdH9MYQfhM9ztVXWSy2J+goOk0k812oAZQ1d8A3yB8a5lHmGmcQ/gmiKq+Q1gS3ImwlLOY8Db7cVV9qIXZ3kn4rrwI+AzwCVVdp6qvE77L/pfwoNuBoG2csDvwrIgsJyjlfFVV31fVZYQH+YmEWexs4BeEm5lYnu7R/xrg6izBNGwnO5Iwg11IGAB2akkh41LYDwhv1rMIb94ntiQtgiJSF8LM7BnCsqnlZMI3zwUERa+bCQNxFtcRluc+JCitPdNCubK4DDhZRFJnPao6gzBIfJd8v/t/QEVsz3MJCoCLCLP/u0zcNwmKhe/FZeQhhIf3K4Q++iCh/Jk0lH9TCteEvvoIYaY6W0SSz0PfJgxyz0j43PBvwuyyNRlJeKFLZsmrCLsgEr5E6EdzCXX4RVWdAiAiI6TQ9sEvCHX6MuG+/zpwnKoubqFsJxCW+t8wM9tL47XhFNZfJvHl+wKCUl5r01D9KWGZeyahX/4a+Jqq3hmv1xBezpcTdpf8l3D/AyAigwmrEXe0gdwdHlFNW0lz0ogzjmeAC1T1ylLL46QjIjcTFN1auurRGjL8nbC9545SyeBsGojIy8BBqrqg1LK0FSLyf8C7qvrnUsuyKeIDdTMRkR0Is5FLooKUU2JEZHfCCsD7hNWFO4C9VPWlUsrlOI7TGmzWS98tQVVfVdWf+CBdVgwCHiMsu/2esKTpg7TjOO2KiFwlInNF5LWM6yIiv5dganWyiExoUro+o3Ycx3GcjUdE9iNMGK5T1fEp148k2GU4EtiToHC3Z2Pp+ozacRzHcVoBVX2C8Bkui0mEQVxV9RmCPYiG7BcAYZ9buyAiGwiHWwhhW9E5qvp0Q3G6du2qfXsHuxF9evfJ+VdU5t8vEsVauzKgdXX1rge3eS8x+rjr163PuSsL0q6oF9Zi41kK5DORN2zIh1+6LNhZqKurY13K61Kh3Hl3dafqnHv16tVG7srgMAskzV0tqa7Op11RkRdq/fr1Uf4NOb+qqnzX6dEztFGntVCRyNFA/nUZ7ZPG2rX5reqdO3fOuW0sMbLatBN5s/Kw8tl41dXVrKO6XvjUvtaEOrZpW2wdJiVqpDpinun+69bl62rDhnyeXToFd6VpmzrNXy+4XyrS390rbX8w/SCpE9tfbJ9X0yHt/WLbpKLCFDr6J30OYPky85WpJt8u69atM/HyTq3L51kgV8ynwjwH1q3Pp7Fhfb5cFptGp075w59suybuyqp8Hdvy9u7TOy/Hunwath4K+1WSd3qHsHnbeFnpFd4D9dOsTHmm1pcp714Xy9apci2Vph/n4po+ap8bNmvbT2w5bdlsX6usCHUrJmziB7BmbdjoUTt7HosXL26STQRJOSWsEaYQ7G8kXKaqlzUj/lAKjQfNjH6zGorUnifhrFLVnQHiHryf0/BeU3r37s25n/0UAJ847hM5/x7d80afkoedvbFXrcpvU7SDj33Q25tvzpy80amePfPGi5KbsjJj8Jk3b16q3N275+042HwWL16ccz/yyCM5WWtjcYoHizS5hwzJnxz35ptv5ty9evWql4Yd5KwcBTeOwabdpUuXnHvBgqCMunTp0pxf37753SEHH3xwiF8LPXrk28bmb0l7wajIGCDef//9nHvMmLwtFjvI1dTU5NwrVqzIuRN5bftZ7IN+zZr8bq5BgwZRG4zFFbR3kqetP9vvsli2LG/8ypazX79+9fwLBpaMUTur/WbNyt/rtq3GjQhG23r1zG+9tuW194ttd1t226cXLcpvjU36abdu3XJ+tr5t/cydmz88zA54NV3y7VdVGep4/vy80b8nn8xv+63bOm/Lxd63tt5sebp2zduBSfqJ7S9WpoUL0ydCtuzDhuW3mtt8kn5n74vZs/OG/I477ricu2pu3uaQvc9tmyT3sW0P2+42rL3PbL3a/m3bJO1+sGW091ZWnsmzb0iP2oIyJ+Wxfcc+92w7WTlsOVeuzBsZtH0teTbbMtrnTfKs+PTnv1WvfA2R9XxIY8OGDatVdbdmZVBI2k3d6MtCqY6s60nYi+c4juM4JUFEila2GibrJbkZzKTQcuIwgv2LBmnPgbpL3C9YQ7ApfGBaIBE5GzgbCme3juM4jtOaiEizZtStwF3AOSJyE0GZbImGs7obpFRL33sB14nIeC36yBfX+y8DGDJkiKukO47jOG1Gc2bUjSEiNxJOr+svIjMJVuSqAVT1UoJ54SMJVvpWEsxRNy5jq0nYDFT1vyLSn2BSb25j4R3HcRyntWntGbWqntTIdQW+3Nx0SzJQi8i2hDNNGzSZ16dPH4466igA+vfLn6BXqDFakekHhcoaWW9OVlnLKickDZilTLbFFlukymTdVqFjg1H0Oumk0J5du3bNKZNlaVtapRVbhgMPPLBe+A8+yJ9iOWrUqNQyWuw3F5unrbdEWcXWg1W86d27d/i/slA+q8iTpdiWYBVErAKSbY8sRStbBhsmkcuW3SrE2DJa5avOnTvTOZpKT5PVtmlFhsa5LYMN09jnHCtfVn/NyseWZ+DAgTl371711UFsfdt8rLtAS9zkaZWxEiUym57FpjFoUF4RLKvebF9PSO4VgIUDaupdh8I+YJUV03YyZCmE2vu5Kdr8absgstrGKkhlKbVa0nYK/P/27jzMrqrK+/h3UQlkMCaEkISQBAiEIV00BBJEZmUQERNFaJFBHBoaHsH5RRT7dZZBGlFEkBdRWkAaUJqojI2RyRbBAJkgAQlkhBAgDCGEDOv94wx3VdU9qapYdc+tW7/P8+TJrlPnnrP2Ge6uc+/aexclYsV14z1XlEyWbSe+LsYU6150nsaMSb5mHTWoqWod4nUcE9WieM0UJS5WSziL5zSeg2HDhrX5fXs6+x11Wcr4jhqSzLdT0gHmRURESlHj76g3Sc0aanev/6MhIiK9hp6oRURE6lgJWd+bRA21iIj0WmqoRURE6pQ++u4CZpZn8MUs32oZpjHzsChztih7O267WhZt0fCbcXncXlFG5tbDKpnrWRb0hg0beMPa1qsoprg8Zmq++uqrbdZ9443KGMkxAzQek6JtVxvHOG5j2rRpeTkbPnHU0uLjHYd7XLJkSV7OhteM5yxmru6+++55OQ4zGDNDYz3jOZk0KRnpL2b2xnI8Z3EITDPLhxDdcsvKGPPZkJAxM3mnnXaqGkfMPI5DhRb1TsjOQzzuMQu5KIO+ZTZ/rE/l+GRjIPftU32c+JjFHfcfz0kc+DDWP7tmY6ZtyzGh11ZdXtRLIhsKN77ujVWV47rmndWHVa02/GbrbWfHuGjs7o4MB7u+yjjnMd54buL1H4f7Xf33yr1Q1NMi67EQz02Mr2is7/bGIo/laj07NrY87ie7HteuXdtin1mdixq/jmSUx2Mcl1fLLq/WY6G9+QMiffQtIiJSx/RELSIiUuf0RC0iIlKn9EQtIiJSx/QdtYiISJ3TE7WIiEid0hN1F3D3vHtA0UD71QbPrzYAfetyTPuPXX3iX1dZN4HYXSBuO65btO0YX+xCknW1GD58OFkPjKK/7IrqUK1bRuyuE7dXNClHVNSVJjvecdkee+yRl8eNGwck3bPifoq6e7WYqCQ9trEbSFx35cqVeXn06NF5OXbVKpqQITtXzc3N+bKi/cTuY6NGjcq7Z1WbOCDGH8XlRV2EiiboyNYp6rZU1E0lrhOvr+XLK5PSZd2p1vWpdO8pmlgk1jce49g1aNX6VXk5u8aKrp2oI3XLuuu98kplIpHYRS6+ruh+ibHG+zXr6hS3EbvUFXVtinWL10/cf/baou5U22yzTV5+x8Cd83K8X6pdM9W6PkHxNV80EUe19634PhrPdTw+cXmML5vE5s2mN6set6LzVHQNVJvgBIqvzUw8H9nx7siEKjEGPVGLiIjUsYZ7ojazfYFLgd2AzUmmqlzl7hufu09ERKTONOoT9U+A44GbgEnAx4GdNvoKERGROtSw31G7+9Nm1pTOJf0LM/tzN8QlIiLSrRr1ifpNM9sceMzMLgSWAQPbeQ1mNgL4IbAv8ArwNnChu9/Syf2LiIh0mUZ8oj6Z5HvpM4EvAGOAj2zsBZak9v03cI27n5Au2w6Y0t7O3D3P5Js+fXq+fMSIEXk5y1yMGYnDwuQXMbPxpZdeyssxyzBmEy5evDgvb7vttkD1THBomeEZM25jJmncfzapA8CyZcuAJKtx6SDa1KEo8/Lll1/OyzHbM8sejZNEZJmZ0DIDNWbUxnVixmg8VllGdMxc3XHHHdvUcf3SymQCMSYonnAke208TjHWOAFElhHcep2Y5flKyBKfduutbeoYJ8jYeuut8/J2222Xl5uamniB5OYdNGhQm1iLxJhiOZ6nOLlGXJ4dn3h9VctohZbXY9FkBo8//nhefmLNTKDlcYrXbuz1ELNs4/GOb2YxozzLXF+wYEG+LJukBapPNNG6btWyhuP+li5dmpcfeb1SjtuOdRg5cmReziariXHFOsb97LDDDnk5rjN//vy8vGLFirwcr6XsWMV7KN6Lxx13XGXbq9/My/GcxGu9Wr3ieS+aiGPmzJlttgEt30OyYx/jHzNmTF7uyEQqDz30EACjBi1t8V6RxRjv/Xje43GN9Ym9O+I5i9d3Fm88Tk19wkRLG5JY4/3enoZ8onb359LiauBbHXzZe4G33f2KVtu5tDP7FhER6UoN+R21mR0NfAfYLn2tAd5O1vc/ATM2OUIREZFu0JBP1MAlwDHALO9Mr/LAzC4DDiB5yp5c5fenAadBy4+wREREulrDPVEDi4DZnWyk5xC+x3b3z5jZMOCRaiu7+5XAlQATJkzYpD8GRERE2tOoT9RnA7eZ2b1Anj3l7hdv5DV/BL5vZme4++XpsgEbWV9ERKTbNeR31MD3gDeAfiQjk7XL3d3MPgT80MzOBl4EVgFfae+169evzzOo99xzz3x5zNbNsghjNmHMkI3ZjlkWd2tvvlnJwozZzNlri7KxY5ZtzJSMWYd/+ctf8nLM8t1nn30AGDt2bJ71HTNx47aLsrejbNsxIzJmxxf91RjrFuOL+8/ECzpmZmYZ06O2gIEDK7314rGK4rarrROPZSzHLNUYS/yAJ2abHnPMMUDLMbXjdVJ0jLfZZhua0rG+4znJ9hOPUzzecd2YiRszWuP5i+tXiy+emyjGXVSfXXfdNS/vvfMuQMtjFrOui8avj2IsMQs7y2KPmb3xfBSdp3jcZs+enZeXLFkCtMzoPvroo/Py6btVxnuPdY+9LuK1WW3s7Xhunn322bwc3x/i64rui3gus1ji9RfrG2MassWQvBxjicc+e0+K+47vZfG4xntk/Pjxlf2EcxLvs2yfMVs7bq9ozPO4jd133x2AUYO2anF/VXvPLLpGo7jtGEvsQZBtO24vyo7DhZdfW/X3RRqxoR7q7kd0difuvoxkRDMREZG60Kgfff+PmR3h7nd1SzQiIiI10lM++q7+GUKxzwB3mNlbZvZ6+q/jvctFRETqSJ8+fTr8ryPM7Egzm2dmT5vZOVV+f4iZvWpmj6X//m+7MXamQu4+qP21RERE6l9XP1GbWRNwGXA4sBh42MymufvcVqve7+5Ht9lAgU5/OG9mU4CD0h//5O6/7+w2REREytYN31HvAzzt7s+k278BmAq0bqg7pbMjk50PTAauSxd9zswOcPc2j/ddoW/fvowYmWQuG5WswGqZwkVj0kZFmeExEzFmGWbbKcqKLcrAjlmT48aNq/rabDzy/v37kyVzxt/HrNeiscZjrFl2fFy3qFyU1RmzSuOxyrIpY0xx3WzbffoUn4e4vbhOtv+i8YVjNm9HxiCOMQ4dOpSNicfvwQcfzMvr1q1jaZr1HcVxlzOLFi3KyzHLNmYQx14Kccz6LbfcMi9n5y9mY8d6xaz0OAZ3rG8sH3744Xl51DuT7fQLGcnxeoiZykUZujGWmOWbnZ94buK1Ec9TPN5F90h23OJ1GY/TS2F7sb5FvTHiPZptJ8Y6enQlizyev7iNuJ9Yn7jtbP8xjmw8f4Dvfe97efmN+ZXjF7cXz0l2XIuu/6Kx2j/ykcrUC0VjXmd1i/t+8cUXq64b9xPP3/777w/AK4OWthjTPLvW45j28VzGfVZ7H4CWme7Vxs8vOh/ZNjo7Flcnn6iHmVkcA+TKdOyPzLYk441kFgPvqrKdd5vZ48BS4MvuPmdjO+3snxJHAXu6+wYAM7sGeBToloZaRESku2zCE/UKd5+0sU1WWdb6L4cZwHbu/oaZHUUyadX4Nq8KOptMBjAklAdvwutFRERKl31H3dF/HbCYZFbJzGiSp+acu7/m7m+k5duAvulonYU6+0T9feBRM5tO8pfDQcBXO7kNERGR0nXDd9QPA+PNbAdgCcn4ISe02udI4IV0MLB9SB6YX2qzpaDDEZrZZsAGYF+S76kN+Iq7P7/RF4qIiNSprsz6dvd1ZnYmcCfQBFzt7nPM7PT091cAxwJnmNk6kimjj29v/owON9TuvsHMznT3G4Fpm1oRERGRetAdI5OlH2ff1mrZFaH8E+AnndlmZyO828y+DPwXyXjd2Y5fbu+FZrYemBUW3eDu52/sNWvXruX5ZckD+5NPPpkvj1mgWXZhHLN33I6VLNJ+W7Qd6xdaZqbGcYcfe+yxvDx27FigZRbhK6+8kpdjZmjMvIzZkWPGVL6uGDy48pV+lpG8fv16ss3EjNGYBVmUNRkzdLP6xGzaQYMq3d7jX41FGdjVslihko0af//444/n5Ww881FLi8enjsc7GxscKhnE1cbUBnj55cqlFcdLjjdXUcZxVs94TGIWbTyXWRY+JPXsR782r83qH+sYz2/RWNbz58/PywcccEBejuOiZ/WJxz3LBIeW11pRVnyM66mnnsrLg2h7TmLmdhzrPmbrxmP5wgsv5OWYKf0/9/wPAM3NzZVt9K9s4/nnKx+4xfpWy6CHynkoymYvelMtur7ja7PrJB6nojiKnrKKxqTOlsd7JB7LM888My9v43tXjTVex3Ec7ky8FuP1Fevzs5/9LC/HXg/xmsn2s8suu+TLjj322Krbi9dX3Oe8efMAWD9oaYvrNHvtyG0qUxQvfG5hXo7XXby3W8y3sFNlvoXN+7atc+ylEGWxxl4R7ekpI5N1tqH+VPr/Z8IyB8ZVWbe11e6+Zyf3JyIi0m0abqxvd9+huwIRERGppUZ9osbM9gO2j6919//swEv7m9lj4efz3P2/qmz/NOA0gOHDh3c2PBERkQ5pyNmzzOxXwI7AY0D2pZkDHWmoO/TRdzrKy5UAu+yyS+eGmBEREemgRn2ingRMaC+VXEREpCdouCdqYDYwEljW3opdoampKc8SnDp1aovlmSzL8F3vqgynGjMpi7KQ498aixcvzsuf+MQn8nK1TNx4UuOYtHGdor9jqo3bm0yfliyL9YrlmEUel8eM1izDO2ZGx/3FuIvG4I4Zq3E/WcZxfF3Mkj7ttNOAJOu7KIs27idmeGYZnEVZ7jEzdOTISiZp0bGK5yQbNzvuu2jc9FgeOHBgPtZ3jCXbTszOjce4aOzilStX5uWY+R8z3bNtF52bous4itfJnnvumZe3G/pim+3FOqxdVylHG9ZX9rnzzjvn5eXLl+fls848q83riq7/uM+YwVxtvOs49nSse9F1HM97PJexznGfmTgedrXzAcVjile7ruLvY9b8+PGVESIHrKx8pRfrHsd5z+6joizzmIEdyxdeeGFejvdzvE6z7cS677hjJdO66P0unr+JEycCMGrQ0qrjxsdzMGnvyoibcXncXov3zzDi5pq3KnFv0S/d9rrq9+36DUk5Ztu3p6GeqM3sdyQfcQ8C5prZX4H8CLr7lA5spvV31Hd012QeIiIi7Wm076inASOA+1stP5hkmLR2uXv9/9kiIiK9SsM8UZPMp/k1d58ZF5rZKuAbwM+7OjAREZHu1FAffQPbt26kAdz9ETPbvmtDEhER6X6N9tF3v438rv9GficiIlK3GumJ+mEzO9Xd/19caGafBv7W9WGJiIh0r0Z7ov48cIuZnUilYZ4EbA58uBviAloexNilJ3azqNYdoFp3DyjuDhC7X7y1JnS5WpOsE7thxL++3l5b6U4Ru7LEyQdit4jY/eLHP/4xkAy+v7Qyd0Yudq2I5VjPnXbaqbL/tAtL7BLyyCOP5OW1b1fq3q9/5QOSuH48xvGYZJNoxMH3jznmmLy89dZbJ/tY23IbUVEXl6xrSTzGsatNtYkrWm+jqGtc1u0udl+JXTficd1qq63y8vr163mNpHtMvNayOhR1mSkSu/3cdNNNeXnBggVtth3PR4w1XjvZ8YaWxy12sRk1alRl22uS3pSVqFveCyNGjMjLRRPAxHMfz/HSpUuBlt314jUf76d4j8TJGeI1nR3veM5uvfXWvLywf+WcxckZ4kQM8VjFayY7VvH62muvvfLyAw88UIk1HIdx4ypTGcR9xkldsuMdj008xscddxydkZ37okl4ihqX2BUwxhKv2azr1/33V3KDL7nkkrxc1L0u3iNHHHEEkHTPmjBhQr48O39NfSr3hW+obC8e+9WhTEE9W9yva5Nz32ISkPC6/v039sFvdQ31HbW7vwDsZ2bvAbJpcv7g7n/stshERES6UaM9UQPg7tOB6d0Ui4iISE01zBO1iIhIo2nIJ2oREZFG0VDfUYuIiDSinvBEbfU8EdbOO+/sV/3w2wA0Nzfny2PmZfbXUMwmjNm8MYu2aJKDODh9XD/uJxMzVGN2a1Em8N///ve8XC2Tuk+fPnnWd1H2ctHED9UG9I/Zr2PGjMnLRdm8cXk8bjHW7LjFdV944YW8nM0bPmpp8eQaRbK443GNGb8xjpgBGo9x0cQGWdZt0V/Msb4xQ3fo0KH5pBxFmeaZeG7i9RXF+kTxWsvqWTRpgbfIbq28Lh7jmIUcJ0cZPfj5NuvGWIuuqSiuH/eTTTJSFFNRNn+8t55//vm8nGUC9w+Z8jGj/MWhba9LaHkeYrZzPH9ZOR7juO7QoUPzcjzXsT5FPUeybRdlysc4Bqys9NaIqt2LsddBUUzxno89DIp6jmTbiccsu4c3tp9qk+mMGrS03Wz0+P5a1PsjHrd4v8S6ZVnnRfdzNiHRlBNPZ9asWW1v1iqam5v95ptv7siqAOy2225/c/dJ7a/Zter/TwkREZFuoO+oRURE6py+oxYREalTeqIWERGpY8r6bsXMRgKXAJOBNcCzwOfdfX6tYhAREcnoiTqwJL3vFuAadz8+XbYnMAIobKhfe+017r33XgB++tOf5sufe+65vJxlYQ4ZMiRf9tJLL+XlmNkYMw5jNmPMeIyZn9l+ijJuY0ZmzDaO68TtxezVs846K1/2VhpWzIStNv4xtMzerDYOcFH2dFFWblFWc9xnteztmKl74403AknWd5Z5CS0zZGPGdhy/O8tSjXWP2cGxPk8++WRejvuJGc6xDlnWexwbu+i4RgsWLMizvuO1lMVY1JMgnpsYX1znwAMPzMvx+FTLSI77juNxx3XisYrZtbHsrz0MtBybPo79Hq+Tooz7eKzGjh3bZnnMBI/XVDzXq1atysvxvozjfg8alHSBiOfsYyd8rFKXLQdTTbx+4vVaNKZ/JmZmR/Epq6gcVcv4j+8JcVz3pY88lZfjPRxjHTlyZJv9xd9X690Ald4k0PL6qTb/QTw3CxcuzMvxOMXrIR6rO++8E0iyvufOndtm/Xj9ZecUWp73mKEer4Gi960XV6wAWo5ZH+uVtQGxfegIPVFXvAdY6+5XZAvc/bEa7VtERKSNnvJEXb3jZ9drRtNhiohIHcm+o+7ovw5u80gzm2dmT5vZOVV+b2b24/T3M81sr2rbieruTwkzOw04DVp+ZCIiItKVuvqJ2syagMuAw4HFwMNmNs3d54bV3g+MT/+9C7g8/b9QrZ6o5wB7d2RFd7/S3Se5+6T4vaaIiEhX6+In6n2Ap939GXd/G7gBmNpqnanAf3riL8AQM9tmYxutVUP9R2ALMzs1W2Bmk83s4BrtX0REpIVu+Oh7W2BR+Hlxuqyz67SMs1ZjfZvZKJLuWXsDb1HpnvXURl7zIrAKWFGDEOvNMFTv3qI31hlU796mVvXezt23bn81MLM7SOLqqH4k7VfmSne/MmzvOOB97v6v6c8nA/u4+1lhnT8A57n7A+nP9wBnu3thHlfNvqN296XAv3TyNVub2SNlDIJeNtW79+iNdQbVu+w4aq0e6+3uR3bxJhcDY8LPo4Glm7BOC7X66FtERKTRPQyMN7MdzGxz4HhgWqt1pgEfT7O/9wVedfdlG9to3WV9i4iI9ETuvs7MzgTuBJqAq919jpmdnv7+CuA24CjgaeBN4JPtbbcnNNRXtr9KQ1K9e4/eWGdQvXubXlFvd7+NpDGOy+JgXw58pjPbrFkymYiIiHSevqMWERGpY3XbULc3DFsjMrMxZjbdzJ4wszlm9rmyY6olM2sys0fN7Pdlx1IrZjbEzG42syfT8/7usmOqBTP7QnqNzzazX5tZv/Zf1fOY2dVmttzMZodlQ83sbjN7Kv1/yzJj7A4F9f5Bep3PNLNbzGxIiSH2KHXZUIdh2N4PTAA+ZmYTyo2qJtYBX3L33YB9gc/0knpnPgc8UXYQNfYj4A533xXYg15QfzPbFvgsMMndm0mSbo4vN6pu80ugdRegc4B73H08cE/6c6P5JW3rfTfQ7O7/TDJr4ldrHVRPVZcNNR0bhq3huPsyd5+Rll8nedPe6Ig1jcLMRgMfAK4qO5ZaMbN3AgcBPwdw97fdfWWpQdVOH6C/mfUBBtBOP9Keyt3vA15utXgqcE1avgb4UC1jqoVq9Xb3u9w9mwv2LyT9h6UD6rWh7vQQa43GzLYHJgIPlRxKrVwCnA20nfy6cY0DXgR+kX7kf5WZDWzvRT2duy8BLgIWAstI+pHeVW5UNTUi6zeb/j+85HjK8Cng9rKD6CnqtaG2Kst6TXq6mb0D+A3JEKuvtbd+T2dmRwPLNzaEXoPqA+wFXO7uE0mGy23Ej0FbSL+TnQrsAIwCBprZSeVGJbViZueSfM13Xdmx9BT12lB3eoi1RmFmfUka6evc/bdlx1Mj+wNTzOxZkq853mtm15YbUk0sBha7e/apyc0kDXejOwxY4O4vuvta4LfAfiXHVEsvZLMlpf8vLzmemjGzU4CjgRNdfYM7rF4b6o4Mw9ZwzMxIvq98wt0vLjueWnH3r7r7aHffnuRc/9HdG/4Jy92fBxaZ2S7pokOBuRt5SaNYCOxrZgPSa/5QekESXTANOCUtnwLcWmIsNWNmRwJfAaa4+5tlx9OT1GVDnSYcZMOwPQHc6O5zyo2qJvYHTiZ5onws/XdU2UFJtzoLuM7MZgJ7At8vN5zul36CcDMwA5hF8j7UkKNWmdmvgf8FdjGzxWb2aeB84HAzewo4PP25oRTU+yfAIODu9L3tio1uRHIamUxERKSO1eUTtYiIiCTUUIuIiNQxNdQiIiJ1TA21iIhIHVNDLSIiUsfUUIuIiNQxNdQiG2FmW4U+7c+b2ZK0/IaZ/bSb9vl5M/t4d2x7U5jZs2Y2bCO/v8HMxtcyJpHeRP2oRTrIzL4JvOHuF3XjPvqQDASyV5hpqFTp0K6T3H1Fwe8PBk5y91NrGphIL6EnapFNYGaHmNnv0/I3zewaM7srffo8xswuNLNZZnZHOn47Zra3md1rZn8zszuz8Z5beS8wI2ukzeyzZjbXzGaa2Q3psoFmdrWZPZzOujU1Xd5kZhel+51pZmelyw9N15uVvm6LdPmzZvYtM5uR/m7XdPlWaV0eNbOfkU6Sk+73D2b2uJnNNrOPpjHfDxyW/pEhIl1MDbVI19iRZD7tqcC1wHR33x1YDXwgbawvBY51972Bq4HvVdnO/kCcRewcYKK7/zNwerrsXJLx0CcD7wF+kE6PeRrJjFTZ+teZWT/gl8BH03j6AGeE7a9w972Ay4Evp8u+ATyQzug1DRibLj8SWOrue7h7M3AHgLtvAJ4G9ujMARORjlFDLdI1bk9ngpoFNJE2YunP2wO7AM2k4xwDXyeZFa61bUjmqM7MJGlwTyKZGhDgCOCcdDt/AvqRNKaHAVdkT+Pu/nK63wXuPj997TXAQWH72Qxtf0vjJP39tek2/gC8EupymJldYGYHuvurYTvLSaasFJEupo+qRLrGGkieLs1sbZjCbwPJfWbAHHd/dzvbWU3S8GY+QNJwTgH+3cz+Kd3WR9x9XnxhOhNV66STanO7t4kbWE/L94M2ySvuPt/M9gaOAs4zs7vc/dvpr/ulsYtIF9MTtUhtzAO2NrN3QzLveNrotvYEsFO6zmbAGHefDpwNDAHeQTKr3Flpw4yZTUxfexdwevZdsZkNBZ4EtjezndJ1TgbubSfW+4AT0228H9gyLY8C3nT3a4GLaDl39s5Ab5jhTqTm1FCL1IC7vw0cC1xgZo8DjwH7VVn1diofTTcB15rZLOBR4IfuvhL4DtAXmGlms9OfAa4imet5ZrqPE9z9LeCTwE3pdjYA7U0v+C3gIDObQfIx+8J0+e7AX9OP3M8FvgtgZiOA1e6+rGNHQ0Q6Q92zROqMmd0CnO3uT5UdS0eY2ReA19z952XHItKI9EQtUn/OIUkq6ylWkiSpiUg30BO1iIhIHdMTtYiISB1TQy0iIlLH1FCLiIjUMTXUIiIidUwNtYiISB1TQy0iIlLH1FCLiIjUMTXUIiIidUwNtYiISB1TQy0iIlLH1FCLiIjUMTXUIiIidUwNtYiISB1TQy0iIlLH1FCLiIjUMTXUIiIidUwNtYiISB1TQy0iIlLH1FCLiIjUMTXUIiIidUwNtYiISB1TQy0iIlLH1FCLiIjUMTXUIiIidaxP2QFszODB7/ThW21ZdhjdatXba+jbf4uyw/iHrVu1hgF9e349MqvWrKPvFgPKDqNLrHt7FQP69y07jC7RCPfLmlXr2KKvrq3u8vSChSvcfeuy4+hKdd1QD99qS566/Sdlh9GtzvjV9Rz13RPKDuMfdu8Z13PRUT2/Hpkzrr+Xo064qOwwusS9vzuDi750VNlhdIlGuF+uP+NeTjhK11Z3sV2nPFd2DF1NH32LiIjUMTXUIiIidUwNtYiISB2r6++oa+XBGXM5/8rf8OdHn2TV6rfYdsRWHHXQ3vzHVz7F5pvXV6JEZ8x7aB6/ueA3PPHgE6x6ZRWDthrE2OaxvP+M97PfMfuVHV4b7s4Op57Kc8uXAzD3ssvYbcyYkqPqWv/6r9uzfHnbr9AuueRRxo3bs/YBdYFGuH962r1SpCdeX41w/XS3Xt9Q3/CH+zjp7ItZv34De+y6A5N3H8+zS17giv+6g+987sQee6E8cNMDXPSxi9iwfgOjdx3N5KMns/r11cx/aD73XX9fXb753DdnTt5IA/xq+nS+//GPlxhR95k8+WhGjtwx/3nw4J6ZpNoI909PvFfa01Our0a4fmqhVzfUb65ew2e+/TPWr9/ASVMO4ZrzP89mmyXfBvx94TIG9OuZ3UDWvLmGy0+/nA3rN3Dg8QfyxV99kaY+TQCsX7+eJfOWlBxhddf+6U8ATBw3jkefeYbr77uP7518MmbGIV/7GvfOns3nPvhBHpo/n8cXLGDqu97F908+mU9deil/nT+fAydM4Povf5mhgwaVW5EOOPzwT7Pvvh8qO4x/SCPcPz31XmlPT7i+GuH6qZVe3VA/OGMuL7/6OgBfP/1f8osEYMex25QV1j9s7oNzef3lpF4f+8bH8jcegKamJsZOGFtWaIXWrF3LzQ8+CMB/fOpTfOT883lu+XLumzOHg5ub8/Uuu+02jj/wQOYuWsQN99/P7TNmcEhzM1sPHsydjz7KxbfeyndPOqmsanTY3Xf/nFmz/pT/fOqpl5QVyiZrhPunJ94rHdETrq9GuH5qpVcnky1/+dW8vN22w0uMpGu9urxSr+HbJ/W65pxrmGJT8n/15vcPP8zKVasYPngwBzc3c/TkyUDlKTtzynvfy6+++EU+vO++AOw8ahT/fe65fOlDHwLg0WeeqWXYm+zhh3/P7373o/xfT9QI909PvFc6oidcX41w/dRKr26ohw8dnJefW7J8I2v2LIOHV+q1YtEKACYcMIGDTzi4rJDalTXIH9xnHzbbbLO8Ib7pwQdZs3Ztvt5uo0cDMGTgQAB22XZbAAb17w/AqrfeqlXI/5Cvfe0Wpk3z/F9P1Aj3T0+8VzqiJ1xfjXD91Eqvbqj3m7gbWw5+BwDfveJGNmzYkP/uuSXLWbt2XVmh/UMm7D+BQUOT72lvOu8m3J3JR0/mw//nwyVHVt0rb7zBbY88AsDP774bmzKFY847D4BXV63id3/9a75u02YtL9nWP0vtNML909PulUbSCNdPrfTq76gHDujHpV8/jY9/5RKunfYnZs1/jn12H8/S5S9z958f44UH/5Mhfd9RdpidtsWALfi3y/6Ni0+8mHt+cQ/PzHiGnd+1My8ufLHs0Kq68YEHeHvdOt45YADv2X33fPncRYt4aulSfjV9eonRSZFGuH962r3SSBrh+qmVXt1QA5z4wUMYM3IYF1z1W/73sSd54u+LGD1yGKced0SPzjo86PiDGDZ6GL+54Dc8+ecnWTR3EYOHD2bi+yay/7H7lx1eC9elH3v/2/vex4Wf/GS+/N7Zsznka1/j9hkz8o+4pb40wv3Tk+6VRtMI108t9PqGGuCgyc0cNLm5/RV7mAkHTGDCARPKDqNd951/ftXlBzc349OmVf3dJaeeyiWnnpr//IlDD+UThx7aLfF1pauuerbsELpcI9w/PeVeaU9PvL4a4frpbvqCT0REpI6poRYREaljaqhFRETqmBpqERGROmbu9dkZHsDMXgfmlR1HCYYBK8oOogSqd++ievcutar3du5en7OQbKJ6z/qe5+6Tyg6i1szsEdW791C9exfVWzpLH32LiIjUMTXUIiIidazeG+oryw6gJKp376J69y6qt3RKXSeTiYiI9Hb1/kQtIiLSq6mhFhERqWN12VCb2ZFmNs/Mnjazc8qOpxbMbIyZTTezJ8xsjpl9ruyYasnMmszsUTP7fdmx1IqZDTGzm83syfS8v7vsmGrBzL6QXuOzzezXZtav7Ji6i5ldbWbLzWx2WDbUzO42s6fS/7csM8buUFDvH6TX+kwzu8XMhpQYYo9Sdw21mTUBlwHvByYAHzOznj+tTfvWAV9y992AfYHP9JJ6Zz4HPFF2EDX2I+AOd98V2INeUH8z2xb4LDDJ3ZuBJuD4cqPqVr8Ejmy17BzgHncfD9yT/txofknbet8NNLv7PwPzga/WOqiequ4aamAf4Gl3f8bd3wZuAKaWHFO3c/dl7j4jLb9O8qbdKyZhNrPRwAeAq8qOpVbM7J3AQcDPAdz9bXdfWWpQtdMH6G9mfYABwNKS4+k27n4f8HKrxVOBa9LyNcCHahlTLVSrt7vf5e7r0h//AoyueWA9VD021NsCi8LPi+klDVbGzLYHJgIPlRxKrVwCnA1sKDmOWhoHvAj8Iv3I/yozG1h2UN3N3ZcAFwELgWXAq+5+V7lR1dwId18GyR/owPCS4ynDp4Dbyw6ip6jHhtqqLOs1fcjM7B3Ab4DPu/trZcfT3czsaGC5u/+t7FhqrA+wF3C5u08EVtGYH4G2kH4fOxXYARgFDDSzk8qNSmrJzM4l+arvurJj6SnqsaFeDIwJP4+mgT8ai8ysL0kjfZ27/7bseGpkf2CKmT1L8jXHe83s2nJDqonFwGJ3zz41uZmk4W50hwEL3P1Fd18L/BbYr+SYau0FM9sGIP1/ecnx1IyZnQIcDZzoGsSjw+qxoX4YGG9mO5jZ5iSJJtNKjqnbmZmRfF/5hLtfXHY8teLuX3X30e6+Pcm5/qO7N/wTlrs/Dywys13SRYcCc0sMqVYWAvua2YD0mj+UXpBE18o04JS0fApwa4mx1IyZHQl8BZji7m+WHU9PUncNdZpscCZwJ8kNfKO7zyk3qprYHziZ5InysfTfUWUHJd3qLOA6M5sJ7Al8v9xwul/6CcLNwAxgFsl7UMMOLWlmvwb+F9jFzBab2aeB84HDzewp4PD054ZSUO+fAIOAu9P3tytKDbIH0RCiIiIidazunqhFRESkQg21iIhIHVNDLSIiUsfUUIuIiNQxNdQiIiJ1TA21yEaY2Vahu9zzZrYkLb9hZj/tpn1+3sw+3h3b3hRm9qyZDdvI728ws/G1jEmkN1H3LJEOMrNvAm+4+0XduI8+JH2M9woTGJQqHTVukruvKPj9wcBJ7n5qTQMT6SX0RC2yCczskGzubDP7ppldY2Z3pU+fx5jZhWY2y8zuSIeGxcz2NrN7zexvZnZnNoxkK+8FZmSNtJl91szmpnP43pAuG5jO9/twOqHH1HR5k5ldlO53ppmdlS4/NF1vVvq6LdLlz5rZt8xsRvq7XdPlW6V1edTMfkY6/n663z+Y2eOWzCX90TTm+4HD0j8yRKSLqaEW6Ro7kkzVORW4Fpju7rsDq4EPpI3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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "import os\n", "import numpy as np\n", "from matplotlib import pyplot as plt\n", "import librosa\n", "\n", "import sys\n", "sys.path.append('..')\n", "import libfmp.b\n", "import libfmp.c3\n", "import libfmp.c4\n", "%matplotlib inline\n", "\n", "def compute_chromagram_from_filename(fn_wav, Fs=22050, N=4096, H=2048, gamma=None, version='STFT', norm='2'):\n", " \"\"\"Compute chromagram for WAV file specified by filename\n", "\n", " Notebook: C5/C5S2_ChordRec_Templates.ipynb\n", "\n", " Args:\n", " fn_wav (str): Filenname of WAV\n", " Fs (scalar): Sampling rate (Default value = 22050)\n", " N (int): Window size (Default value = 4096)\n", " H (int): Hop size (Default value = 2048)\n", " gamma (float): Constant for logarithmic compression (Default value = None)\n", " version (str): Technique used for front-end decomposition ('STFT', 'IIS', 'CQT') (Default value = 'STFT')\n", " norm (str): If not 'None', chroma vectors are normalized by norm as specified ('1', '2', 'max')\n", " (Default value = '2')\n", "\n", " Returns:\n", " X (np.ndarray): Chromagram\n", " Fs_X (scalar): Feature reate of chromagram\n", " x (np.ndarray): Audio signal\n", " Fs (scalar): Sampling rate of audio signal\n", " x_dur (float): Duration (seconds) of audio signal\n", " \"\"\"\n", " x, Fs = librosa.load(fn_wav, sr=Fs)\n", " x_dur = x.shape[0] / Fs\n", " if version == 'STFT':\n", " # Compute chroma features with STFT\n", " X = librosa.stft(x, n_fft=N, hop_length=H, pad_mode='constant', center=True)\n", " if gamma is not None:\n", " X = np.log(1 + gamma * np.abs(X) ** 2)\n", " else:\n", " X = np.abs(X) ** 2\n", " X = librosa.feature.chroma_stft(S=X, sr=Fs, tuning=0, norm=None, hop_length=H, n_fft=N)\n", " if version == 'CQT':\n", " # Compute chroma features with CQT decomposition\n", " X = librosa.feature.chroma_cqt(y=x, sr=Fs, hop_length=H, norm=None)\n", " if version == 'IIR':\n", " # Compute chroma features with filter bank (using IIR elliptic filter)\n", " X = librosa.iirt(y=x, sr=Fs, win_length=N, hop_length=H, center=True, tuning=0.0)\n", " if gamma is not None:\n", " X = np.log(1.0 + gamma * X)\n", " X = librosa.feature.chroma_cqt(C=X, bins_per_octave=12, n_octaves=7,\n", " fmin=librosa.midi_to_hz(24), norm=None)\n", " if norm is not None:\n", " X = libfmp.c3.normalize_feature_sequence(X, norm=norm)\n", " Fs_X = Fs / H\n", " return X, Fs_X, x, Fs, x_dur\n", "\n", "def plot_chromagram_annotation(ax, X, Fs_X, ann, color_ann, x_dur, cmap='gray_r', title=''):\n", " \"\"\"Plot chromagram and annotation\n", "\n", " Notebook: C5/C5S2_ChordRec_Templates.ipynb\n", "\n", " Args:\n", " ax: Axes handle\n", " X: Feature representation\n", " Fs_X: Feature rate\n", " ann: Annotations\n", " color_ann: Color for annotations\n", " x_dur: Duration of feature representation\n", " cmap: Color map for imshow (Default value = 'gray_r')\n", " title: Title for figure (Default value = '')\n", " \"\"\"\n", " libfmp.b.plot_chromagram(X, Fs=Fs_X, ax=ax,\n", " chroma_yticks=[0, 4, 7, 11], clim=[0, 1], cmap=cmap,\n", " title=title, ylabel='Chroma', colorbar=True)\n", " libfmp.b.plot_segments_overlay(ann, ax=ax[0], time_max=x_dur,\n", " print_labels=False, colors=color_ann, alpha=0.1)\n", "\n", "# Compute chroma features\n", "fn_wav = os.path.join('..', 'data', 'C5', 'FMP_C5_F01_Beatles_LetItBe-mm1-4_Original.wav')\n", "N = 4096\n", "H = 2048\n", "X_STFT, Fs_X, x, Fs, x_dur = compute_chromagram_from_filename(fn_wav, N=N, H=H, gamma=0.1, version='STFT')\n", "X_IIR, Fs_X, x, Fs, x_dur = compute_chromagram_from_filename(fn_wav, N=N, H=H, gamma=100, version='IIR')\n", "X_CQT, Fs_X, x, Fs, x_dur = compute_chromagram_from_filename(fn_wav, N=N, H=H, version='CQT')\n", "\n", "# Annotations\n", "fn_ann = os.path.join('..', 'data', 'C5', 'FMP_C5_F01_Beatles_LetItBe-mm1-4_Original_Chords_simplified.csv')\n", "ann, _ = libfmp.c4.read_structure_annotation(fn_ann)\n", "color_ann = {'N': [1, 1, 1, 1], 'C': [1, 0.5, 0, 1], 'G': [0, 1, 0, 1], \n", " 'Am': [1, 0, 0, 1], 'F': [0, 0, 1, 1]}\n", "\n", "# Plot\n", "cmap = libfmp.b.compressed_gray_cmap(alpha=1, reverse=False)\n", "fig, ax = plt.subplots(5, 2, gridspec_kw={'width_ratios': [1, 0.03], \n", " 'height_ratios': [1, 2, 2, 2, 0.5]}, figsize=(7, 8))\n", "libfmp.b.plot_signal(x, Fs, ax=ax[0,0], title='Waveform of audio signal')\n", "libfmp.b.plot_segments_overlay(ann, ax=ax[0,0], time_max=x_dur,\n", " print_labels=False, colors=color_ann, alpha=0.1)\n", "ax[0,1].axis('off')\n", "\n", "title = 'STFT-based chromagram (feature rate = %0.1f Hz, N = %d)'%(Fs_X, X_STFT.shape[1])\n", "plot_chromagram_annotation([ax[1, 0], ax[1, 1]], X_STFT, Fs_X, ann, color_ann, x_dur, title=title)\n", "\n", "title = 'IIR-based chromagram (feature rate = %0.1f Hz, N = %d)'%(Fs_X, X_IIR.shape[1])\n", "plot_chromagram_annotation([ax[2, 0], ax[2, 1]], X_IIR, Fs_X, ann, color_ann, x_dur, title=title)\n", "\n", "title = 'CQT-based chromagram (feature rate = %0.1f Hz, N = %d)'%(Fs_X, X_CQT.shape[1])\n", "plot_chromagram_annotation([ax[3, 0], ax[3, 1]], X_CQT, Fs_X, ann, color_ann, x_dur, title=title)\n", "\n", "libfmp.b.plot_segments(ann, ax=ax[4, 0], time_max=x_dur, time_label='Time (seconds)',\n", " colors=color_ann, alpha=0.3)\n", "ax[4,1].axis('off')\n", "plt.tight_layout()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Template-Based Pattern Matching\n", "\n", "Given the chroma sequence $X=(x_1,x_2,\\ldots,x_N)$ and a set $\\Lambda$ of possible chord labels, the objective of the next step is to map each chroma vector $x_n\\in\\mathbb{R}^{12}$ to a chord label $\\lambda_{n} \\in \\Lambda$, $n\\in[1:N]$. For example, one may consider the set\n", "\n", "\\begin{equation}\n", " \\Lambda = \\{\\mathbf{C},\\mathbf{C}^\\sharp,\\ldots,\\mathbf{B},\\mathbf{Cm},\\mathbf{C^\\sharp m},\\ldots,\\mathbf{Bm}\\}\n", "\\end{equation}\n", "\n", "consisting of the [twelve major and twelve minor triads](../C5/C5S1_Chords.html). In this case, each frame $n\\in[1:N]$ is assigned to a [major chord](../C5/C5S1_Chords.html) or a [minor chord](../C5/C5S1_Chords.html) specified by $\\lambda_{n}$. For the pattern matching step, we now introduce a simple template-based approach. The idea is to precompute a set \n", "\n", "$$\n", "\\mathcal{T}\\subset\\mathcal{F}=\\mathbb{R}^{12}\n", "$$ \n", "\n", "of templates denoted by $\\mathbf{t}_\\lambda\\in\\mathcal{T}$, $\\lambda\\in\\Lambda$. Intuitively, each template can be thought of as a prototypical chroma vector that represents a specific musical chord. Furthermore, we fix a similarity measure \n", "\n", "\\begin{equation}\n", "s:\\mathcal{F}\\times\\mathcal{F}\\to \\mathbb{R}\n", "\\end{equation}\n", "\n", "that allows for comparing different chroma vectors. Then, the template-based procedure consists in assigning the chord label that maximizes the similarity between the corresponding template and the given feature vector $x_n$:\n", "\n", "\\begin{equation}\n", " \\lambda_{n} := \\underset{\\lambda \\in \\Lambda}{\\mathrm{argmax}}\n", " \\,\\, s( \\mathbf{t}_\\lambda , x_n ).\n", "\\end{equation}\n", "\n", "In this procedure, there are many design choices that crucially influence the performance of a chord recognizer. \n", "\n", "* Which chords should be considered in $\\mathcal{T}$? \n", "* How are the chord templates defined? \n", "* What is a suitable similarity measure to compare the feature vectors with the chord templates? \n", "\n", "To obtain a first simple chord recognition system, we make the following design choices. For the chord label set $\\Lambda$, we choose the twelve major and twelve minor triads. This choice, even though problematic from a musical point of view, is convenient and instructive. Considering chords up to enharmonic equivalence and up to octave shifts, each triad can be encoded by a three-element subset of $[0:11]$. For example, the $\\mathrm{C}$ major chord $\\mathbf{C}$ corresponds to the subset $\\{0,4,7\\}$. Each subset, in turn, can be identified with a binary twelve-dimensional chroma vector $x=(x(0),x(1),\\ldots,x(11))^\\top$, where $x(i)=1$ if and only if the chroma value $i\\in[0:11]$ is contained in the chord. For example, in the case of the $\\mathrm{C}$-major chord $\\mathbf{C}$, the resulting chroma vector is\n", "\n", "\\begin{equation}\n", "\\label{eq:ChordReco:Template:Basic:ChromaVectC}\n", " \\mathbf{t}_{\\mathbf{C}}{} := x =(1,0,0,0,1,0,0,1,0,0,0,0)^\\top.\n", "\\end{equation}\n", "\n", "Using a chroma-based encoding, the twelve major chords and twelve minor chords can be obtained by [cyclically shifting](../C3/C3S1_TranspositionTuning.html) the binary vectors for the $\\mathrm{C}$-major and the $\\mathrm{C}$-minor triads, respectively. The result is illustrated by the following figure.\n", "\n", "\"FMP_C5_F06\"\n", "\n", "For comparing chroma features and chord templates, we use in the following a simple similarity measure using the inner product of normalized vectors:\n", "\n", "\\begin{equation}\n", " s(x,y)= \\frac{\\langle x,y\\rangle}{\\|x\\|\\cdot\\|y\\|}\n", "\\end{equation}\n", "\n", "for $x,y\\in\\mathcal{F}$ with $\\|x\\|\\not= 0$ and $\\|y\\|\\not= 0$. In the case $\\|x\\|=0$ or $\\|y\\|=0$, we set $s(x,y)=0$. Note that this measure always yields a value $s(x,y)\\in[-1,1]$. In the case that the vectors $x$ and $y$ only have positive entries, one has $s(x,y)\\in[0,1]$." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Implementation\n", "\n", "In the following code cell, we provide an implementation for the template-based chord recognition procedure described before. To obtain a better understanding of this procedure, we continue our Beatles example from above. The following steps are performed and visualized:\n", "\n", "* First, the audio recording is converted into a chroma representation. As an example, we use the STFT-variant as computed before. \n", "\n", "* Second, each chroma vector is compared with each of the $24$ binary chord templates, which yields $24$ similarity values per frame. These similarity values are visualized in the form of a **time–chord representation**.\n", "\n", "* Third, we select for each frame the chord label $\\lambda_{n}$ of the template that maximizes the similarity value over all $24$ chord templates. This yields our final chord recognition result, which is shown in the form of a **binary time–chord representation**.\n", "\n", "* Fourth, the manually generated chord annotations are visualized.\n", "\n", "In the following figure, all visualizations are superimposed with the manually generated chord annotations. \n", "\n", "" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "execution": { "iopub.execute_input": "2024-02-15T08:58:10.034493Z", "iopub.status.busy": "2024-02-15T08:58:10.033998Z", "iopub.status.idle": "2024-02-15T08:58:11.020877Z", "shell.execute_reply": "2024-02-15T08:58:11.020317Z" } }, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "def get_chord_labels(ext_minor='m', nonchord=False):\n", " \"\"\"Generate chord labels for major and minor triads (and possibly nonchord label)\n", "\n", " Notebook: C5/C5S2_ChordRec_Templates.ipynb\n", "\n", " Args:\n", " ext_minor (str): Extension for minor chords (Default value = 'm')\n", " nonchord (bool): If \"True\" then add nonchord label (Default value = False)\n", "\n", " Returns:\n", " chord_labels (list): List of chord labels\n", " \"\"\"\n", " chroma_labels = ['C', 'C#', 'D', 'D#', 'E', 'F', 'F#', 'G', 'G#', 'A', 'A#', 'B']\n", " chord_labels_maj = chroma_labels\n", " chord_labels_min = [s + ext_minor for s in chroma_labels]\n", " chord_labels = chord_labels_maj + chord_labels_min\n", " if nonchord is True:\n", " chord_labels = chord_labels + ['N']\n", " return chord_labels\n", "\n", "def generate_chord_templates(nonchord=False):\n", " \"\"\"Generate chord templates of major and minor triads (and possibly nonchord)\n", "\n", " Notebook: C5/C5S2_ChordRec_Templates.ipynb\n", "\n", " Args:\n", " nonchord (bool): If \"True\" then add nonchord template (Default value = False)\n", "\n", " Returns:\n", " chord_templates (np.ndarray): Matrix containing chord_templates as columns\n", " \"\"\"\n", " template_cmaj = np.array([1, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0]).T\n", " template_cmin = np.array([1, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0]).T\n", " num_chord = 24\n", " if nonchord:\n", " num_chord = 25\n", " chord_templates = np.ones((12, num_chord))\n", " for shift in range(12):\n", " chord_templates[:, shift] = np.roll(template_cmaj, shift)\n", " chord_templates[:, shift+12] = np.roll(template_cmin, shift)\n", " return chord_templates\n", "\n", "def chord_recognition_template(X, norm_sim='1', nonchord=False):\n", " \"\"\"Conducts template-based chord recognition\n", " with major and minor triads (and possibly nonchord)\n", "\n", " Notebook: C5/C5S2_ChordRec_Templates.ipynb\n", "\n", " Args:\n", " X (np.ndarray): Chromagram\n", " norm_sim (str): Specifies norm used for normalizing chord similarity matrix (Default value = '1')\n", " nonchord (bool): If \"True\" then add nonchord template (Default value = False)\n", "\n", " Returns:\n", " chord_sim (np.ndarray): Chord similarity matrix\n", " chord_max (np.ndarray): Binarized chord similarity matrix only containing maximizing chord\n", " \"\"\"\n", " chord_templates = generate_chord_templates(nonchord=nonchord)\n", " X_norm = libfmp.c3.normalize_feature_sequence(X, norm='2')\n", " chord_templates_norm = libfmp.c3.normalize_feature_sequence(chord_templates, norm='2')\n", " chord_sim = np.matmul(chord_templates_norm.T, X_norm)\n", " if norm_sim is not None:\n", " chord_sim = libfmp.c3.normalize_feature_sequence(chord_sim, norm=norm_sim)\n", " # chord_max = (chord_sim == chord_sim.max(axis=0)).astype(int)\n", " chord_max_index = np.argmax(chord_sim, axis=0)\n", " chord_max = np.zeros(chord_sim.shape).astype(np.int32)\n", " for n in range(chord_sim.shape[1]):\n", " chord_max[chord_max_index[n], n] = 1\n", "\n", " return chord_sim, chord_max\n", "\n", "# Chord recognition\n", "X = X_STFT\n", "chord_sim, chord_max = chord_recognition_template(X, norm_sim='max')\n", "chord_labels = get_chord_labels(nonchord=False)\n", "\n", "# Plot\n", "cmap = libfmp.b.compressed_gray_cmap(alpha=1, reverse=False)\n", "fig, ax = plt.subplots(4, 2, gridspec_kw={'width_ratios': [1, 0.03], \n", " 'height_ratios': [1.5, 3, 3, 0.3]}, figsize=(8, 10))\n", "libfmp.b.plot_chromagram(X, ax=[ax[0,0], ax[0,1]], Fs=Fs_X, clim=[0, 1], xlabel='',\n", " title='STFT-based chromagram (feature rate = %0.1f Hz)' % (Fs_X))\n", "libfmp.b.plot_segments_overlay(ann, ax=ax[0,0], time_max=x_dur,\n", " print_labels=False, colors=color_ann, alpha=0.1)\n", "\n", "libfmp.b.plot_matrix(chord_sim, ax=[ax[1, 0], ax[1, 1]], Fs=Fs_X, \n", " title='Time–chord representation of chord similarity matrix',\n", " ylabel='Chord', xlabel='')\n", "ax[1, 0].set_yticks(np.arange( len(chord_labels) ))\n", "ax[1, 0].set_yticklabels(chord_labels)\n", "libfmp.b.plot_segments_overlay(ann, ax=ax[1, 0], time_max=x_dur,\n", " print_labels=False, colors=color_ann, alpha=0.1)\n", "\n", "libfmp.b.plot_matrix(chord_max, ax=[ax[2, 0], ax[2, 1]], Fs=Fs_X, \n", " title='Time–chord representation of chord recognition result',\n", " ylabel='Chord', xlabel='')\n", "ax[2, 0].set_yticks(np.arange( len(chord_labels) ))\n", "ax[2, 0].set_yticklabels(chord_labels)\n", "ax[2, 0].grid()\n", "libfmp.b.plot_segments_overlay(ann, ax=ax[2, 0], time_max=x_dur,\n", " print_labels=False, colors=color_ann, alpha=0.1)\n", "\n", "libfmp.b.plot_segments(ann, ax=ax[3, 0], time_max=x_dur, time_label='Time (seconds)',\n", " colors=color_ann, alpha=0.3)\n", "ax[3, 1].axis('off')\n", "plt.tight_layout()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The chord similarity values shown in the form of a time–chord representation indicate for each chroma vector a kind of likelihood for the $24$ possible chords. For example, this visualization shows that the chroma vectors at the beginning of the Beatles song are most similar to the template for the $\\mathrm{C}$ major chord $\\mathbf{C}$. Furthermore, there is also a higher degree of similarity to the templates for $\\mathbf{C}$, $\\mathbf{Em}$, and $\\mathbf{Am}$. Comparing the final chord recognition results with the reference annotation, one can observe that the results obtained from the automated procedure agree with the reference labels for most of the frames. We continue with our discussion of the results in the [FMP notebook on chord recognition evaluation](../C5/C5S2_ChordRec_Eval.html), where we also consider further examples.\n", "\n", "We close our Beatles example by looking at the similarity-maximizing chord templates for each frame. This yields a time‐chroma representation, which can be compared with the original input chromagram. In a way, the sequence of chord templates may be thought of as a musically informed quantization of the input chroma representation." ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "execution": { "iopub.execute_input": "2024-02-15T08:58:11.023350Z", "iopub.status.busy": "2024-02-15T08:58:11.023171Z", "iopub.status.idle": "2024-02-15T08:58:11.415278Z", "shell.execute_reply": "2024-02-15T08:58:11.414649Z" } }, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "chord_templates = generate_chord_templates() \n", "X_chord = np.matmul(chord_templates, chord_max)\n", "\n", "# Plot\n", "fig, ax = plt.subplots(3, 2, gridspec_kw={'width_ratios': [1, 0.03], \n", " 'height_ratios': [1, 1, 0.2]}, figsize=(8, 5))\n", "\n", "libfmp.b.plot_chromagram(X, ax=[ax[0, 0], ax[0, 1]], Fs=Fs_X, clim=[0, 1], xlabel='',\n", " title='STFT-based chromagram (feature rate = %0.1f Hz)' % (Fs_X))\n", "libfmp.b.plot_segments_overlay(ann, ax=ax[0, 0], time_max=x_dur,\n", " print_labels=False, colors=color_ann, alpha=0.1)\n", "\n", "libfmp.b.plot_chromagram(X_chord, ax=[ax[1, 0], ax[1, 1]], Fs=Fs_X, clim=[0, 1], xlabel='',\n", " title='Binary templates of the chord recognition result')\n", "libfmp.b.plot_segments_overlay(ann, ax=ax[1, 0], time_max=x_dur,\n", " print_labels=False, colors=color_ann, alpha=0.1)\n", "\n", "libfmp.b.plot_segments(ann, ax=ax[2, 0], time_max=x_dur, time_label='Time (seconds)',\n", " colors=color_ann, alpha=0.3)\n", "ax[2,1].axis('off')\n", "plt.tight_layout()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Further Notes\n", "\n", "In this notebook, we have studied the problem of chord recognition with the objective of automatically extracting chord labels from a given music recording. Only considering the $24$ major and minor triads in a simplistic scenario, we introduced a first template-based matching procedure that compares chroma features of the music recording with prototypical chroma templates of the triads. Already with this simple baseline approach, there are many design choices that have a substantial influence on the final chord recognition results. \n", "\n", "* In this notebook, we have used **idealized binary chord templates** that indicate the presence or absence of notes in the given chord. For real music recordings, however, the presence of [harmonics](../C1/C1S3_HarmonicSeries.html) and other sound components leads to chroma features where the energy is spread over the chroma bands in a more unstructured, non-binary fashion. This motivates the usage of **chord templates with harmonics**, where the chroma patterns also account for the harmonics of the chord notes.\n", "\n", "* Instead of explicitly modeling the harmonics, a conceptually different approach is to **learn** chroma patterns from labeled training data. The input of such a **supervised learning** procedure consists of pairs of a chroma vector and a corresponding chord label. A simple way is then to derive chord templates by suitably averaging chroma vectors that are labeled with the same chord. \n", "\n", "* Taking the average templates is a simple way to adapt a template-based chord recognizer to given training data. More involved approaches are often based on statistical models that capture not only the **averages** but also the **variances** in the training data. In such approaches, the templates are replaced by chord models that are specified by, e.g., **Gaussian distributions** given in terms of a mean vector and a covariance matrix. The similarity of a given chroma vector to a chord model is then expressed by a Gaussian probability value and the assigned label is determined by the probability-maximizing chord model. The discussion of such statistical approaches is beyond the scope of these notebooks, and we refer the reader to the overview article by Cho and Bello.\n", "\n", "* Besides refining and adapting the chord templates, another general strategy is to modify and enhance the chroma features extracted from the audio recordings to be analyzed. We have already seen at the beginning of this notebook that there are many **chroma variants** with quite different properties. The chroma type used has a strong influence on the chord recognition results, as has been demonstrated by Jiang et al. in their article on **Analyzing Chroma Feature Types for Automated Chord Recognition**.\n", "\n", "Some of these components and more elaborate procedures for automated chord recognition are discussed in the subsequent notebooks.\n", "\n", "* In the [FMP notebook on chord recognition evaluation](../C5/C5S2_ChordRec_Eval.html), we introduce some evaluation measures and discuss further phenomena and examples.\n", "* In the [FMP notebook on HMM-based chord recognition](../C5/C5S3_ChordRec_HMM.html), we discuss a chord recognition procedure that applies a context-aware postfiltering technique.\n", "* In the [FMP notebook on the Beatles collection](../C5/C5S3_ChordRec_Beatles.html), we present a case study that indicates the relevance of various chord recognition components. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "
\n", "Acknowledgment: This notebook was created by Meinard Müller and Christof Weiß.\n", "
" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "\n", "
\"C0\"\"C1\"\"C2\"\"C3\"\"C4\"\"C5\"\"C6\"\"C7\"\"C8\"
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