{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "
\n", "\"FMP\"\n", "\"AudioLabs\"\n", "
" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "
\n", "\"C6\"\n", "

Autocorrelation Tempogram

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

\n", "Following Section 6.2.3 of [Müller, FMP, Springer 2015], we show in this notebook how to compute a tempogram using a short-time autocorrelation method. A MATLAB implementation of this approach can be found in the Tempogram Toolbox.\n", " \n", "

\n", "

" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Autocorrelation\n", "\n", "As a second periodicity estimation method, besides the Fourier-based approach, we now discuss an autocorrelation-based approach. Generally speaking, the **autocorrelation** is a mathematical tool for measuring the similarity of a signal with a time-shifted version of itself. Let $x:\\mathbb{Z}\\to\\mathbb{R}$ be such discrete-time signal having finite energy. The autocorrelation $R_{xx}:\\mathbb{Z}\\to\\mathbb{R}$ of the real-valued signal $x$ is defined by \n", "\n", "\\begin{equation}\n", "R_{xx}(\\ell) = \\sum_{m\\in\\mathbb{Z}} x(m)x(m-\\ell),\n", "\\end{equation}\n", "\n", "which yields a function that depends on the time-shift or **lag** parameter $\\ell\\in\\mathbb{Z}$. The autocorrelation $R_{xx}(\\ell)$ is maximal for $\\ell=0$ and symmetric in $\\ell$. Intuitively, if the autocorrelation is large for a given lag, then the signal contains repeating patterns that are separated by a time period as specified by the lag parameter.\n", "\n", "In the following example, we consider a signal that consists of two click track patterns: one with period $9$ and one with period $14$. The autocorrelation has trivially a peak at lag $\\ell=0$ (the signal is similar to itself), then at $\\ell=9$ (first period), $\\ell=14$ (second period), $\\ell=18$ (twice the first period), $\\ell=27$ (three times the first and, at $\\ell=28$, twice the second period), and so on. In particular, if the autocorrelation has a peak at lag $\\ell$, then it has peaks also at integer multiples of $\\ell$. " ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "execution": { "iopub.execute_input": "2024-02-15T08:49:24.219696Z", "iopub.status.busy": "2024-02-15T08:49:24.219410Z", "iopub.status.idle": "2024-02-15T08:49:27.194786Z", "shell.execute_reply": "2024-02-15T08:49:27.194125Z" } }, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "import os, sys\n", "import librosa\n", "import numpy as np\n", "from scipy import signal\n", "from scipy.interpolate import interp1d\n", "from matplotlib import pyplot as plt\n", "import matplotlib.gridspec as gridspec\n", "import IPython.display as ipd\n", "\n", "sys.path.append('..')\n", "import libfmp.b\n", "import libfmp.c2\n", "import libfmp.c6\n", "%matplotlib inline\n", "\n", "N = 100\n", "x = np.zeros(N)\n", "x[np.arange(1,N,14)] = 1\n", "x[np.arange(3,N,9)] = 1\n", "\n", "fig, ax = plt.subplots(3, 1, gridspec_kw={'height_ratios': [1, 1.5, 1.5]}, figsize=(6, 5)) \n", "\n", "ax[0].plot(x, 'k')\n", "ax[0].set_title('Signal $x$')\n", "ax[0].set_xlabel('Time (samples)')\n", "\n", "r_xx = np.correlate(x, x, mode='full')\n", "lag_axis = np.arange(-(N-1),N)\n", "ax[1].plot(lag_axis,r_xx, 'r')\n", "ax[1].set_title('Autocorrelation (full mode)')\n", "ax[1].set_xlabel(r'Lag parameter (samples)')\n", "\n", "r_xx = np.correlate(x, x, mode='full')\n", "lag_axis = np.arange(-(N-1),N)\n", "ax[2].plot(lag_axis,r_xx, 'r')\n", "ax[2].set_title('Autocorrelation (full mode)')\n", "ax[2].set_xlabel(r'Lag parameter (samples)')\n", "ax[2].set_xlim([0,50])\n", "ax[2].set_ylim([0,11])\n", "\n", "plt.tight_layout()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Short-Time Autocorrelation\n", "\n", "We now apply the autocorrelation in a local fashion for analyzing a given novelty function $\\Delta:\\mathbb{Z}\\to\\mathbb{R}$ in the neighborhood of a given time parameter $n$. To this end, we fix a window function $w:\\mathbb{Z}\\to\\mathbb{R}$. The windowed version $\\Delta_{w,n}:\\mathbb{Z}\\to\\mathbb{R}$ localized at point $n\\in\\mathbb{Z}$ is defined by\n", "\n", "\\begin{equation}\n", " \\Delta_{w,n}(m):=\\Delta(m)w(m-n),\n", "\\end{equation}\n", "\n", "$m\\in\\mathbb{Z}$. To obtain the **short-time autocorrelation** $\\mathcal{A}:\\mathbb{Z}\\times\\mathbb{Z}\\to\\mathbb{R}$, we compute the autocorrelation of $\\Delta_{w,n}$:\n", "\n", "\\begin{equation}\n", "\\mathcal{A}(n,\\ell) := \\sum_{m\\in\\mathbb{Z}}\\Delta(m)w(m-n)\\Delta(m-\\ell) w(m-n-\\ell).\n", "\\end{equation}\n", "\n", "In the following, we assume that the window function $w:[0:N-1]\\to\\mathbb{R}$ has finite length $N\\in\\mathbb{N}$. (As usual, for mathematical convenience, one may extend $w$ by zero outside the interval $[0:N-1]$.) Then the autocorrelation of the localized novelty function is zero for all but a finite number of time lag parameters. More precisely, one can show that $\\mathcal{A}(n,\\ell)=0$ for $|\\ell|> N$. Because of this property and the symmetry of the autocorrelation, one only needs to consider the time lag parameters $\\ell\\in[0:N-1]$. Furthermore, because of the windowing, at most $N-\\ell$ of the summands are nonzero. To balance out the effect of the windowing, the value $\\mathcal{A}(n,\\ell)$ may be divided by a factor that depends on the window properties and the overlap $N-\\ell$ of the window and its time-shifted version. Visualizing the short-time autocorrelation $\\mathcal{A}$ leads to a **time–lag representation** with a frame parameter $n\\in\\mathbb{Z}$ and a lag parameter $\\ell\\in[0:N-1]$." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Implementation\n", "\n", "In the following, we provide an implementation for computing the autocorrelation tempogram. The novelty curve $\\Delta$ is assumed to have length $L\\in\\mathbb{N}$ with a sampling rate $F_\\mathrm{s}^\\Delta$. As window function $w$, we choose a rectangular window of length $N\\in\\mathbb{N}$. Furthermore, we introduce a hopsize parameter $H\\in\\mathbb{N}$. We use a [**centered view**](../C2/C2_STFT-Conventions.html), where the novelty function is zero-padded by half the window length. As a result, the first frame of $\\mathcal{A}$ indexed by $n=0$ corresponds to the physical time position $t=0~\\mathrm{sec}$. Furthermore, the hopsize parameter $H$ reduces the frame rate of the tempogram to $F_\\mathrm{s}^\\Delta/H$. Our algorithm loops over the frame indices $n$ and computes $\\mathcal{A}(n,\\ell)$ for all $\\ell\\in[0:N-1]$ using a single autocorrelation. As an illustrating example, we use a small excerpt of a recording of the Waltz No. 2 by Dimitri Shostakovich's Suite for Variety Orchestra No. 1. Furthermore, we use the parameters $F_\\mathrm{s}^\\Delta = 100~\\mathrm{Hz}$ and $N=300$." ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "execution": { "iopub.execute_input": "2024-02-15T08:49:27.227610Z", "iopub.status.busy": "2024-02-15T08:49:27.227306Z", "iopub.status.idle": "2024-02-15T08:49:28.257464Z", "shell.execute_reply": "2024-02-15T08:49:28.256855Z" } }, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# @jit(nopython=True) # not possible because of np.correlate with mode='full'\n", "def compute_autocorrelation_local(x, Fs, N, H, norm_sum=True):\n", " \"\"\"Compute local autocorrelation [FMP, Section 6.2.3]\n", "\n", " Notebook: C6/C6S2_TempogramAutocorrelation.ipynb\n", "\n", " Args:\n", " x (np.ndarray): Input signal\n", " Fs (scalar): Sampling rate\n", " N (int): Window length\n", " H (int): Hop size\n", " norm_sum (bool): Normalizes by the number of summands in local autocorrelation (Default value = True)\n", "\n", " Returns:\n", " A (np.ndarray): Time-lag representation\n", " T_coef (np.ndarray): Time axis (seconds)\n", " F_coef_lag (np.ndarray): Lag axis\n", " \"\"\"\n", " # L = len(x)\n", " L_left = round(N / 2)\n", " L_right = L_left\n", " x_pad = np.concatenate((np.zeros(L_left), x, np.zeros(L_right)))\n", " L_pad = len(x_pad)\n", " M = int(np.floor(L_pad - N) / H) + 1\n", " A = np.zeros((N, M))\n", " win = np.ones(N)\n", " if norm_sum is True:\n", " lag_summand_num = np.arange(N, 0, -1)\n", " for n in range(M):\n", " t_0 = n * H\n", " t_1 = t_0 + N\n", " x_local = win * x_pad[t_0:t_1]\n", " r_xx = np.correlate(x_local, x_local, mode='full')\n", " r_xx = r_xx[N-1:]\n", " if norm_sum is True:\n", " r_xx = r_xx / lag_summand_num\n", " A[:, n] = r_xx\n", " Fs_A = Fs / H\n", " T_coef = np.arange(A.shape[1]) / Fs_A\n", " F_coef_lag = np.arange(N) / Fs\n", " return A, T_coef, F_coef_lag\n", "\n", "#fn_wav = os.path.join('..', 'data', 'C6', 'FMP_C6_F11_ClickTrack-BPM170-200.wav')\n", "fn_wav = os.path.join('..', 'data', 'C6', 'FMP_C6_F07_Shostakovich_Waltz-02-Section_IncreasingTempo.wav')\n", "Fs = 22050\n", "x, Fs = librosa.load(fn_wav, Fs) \n", "\n", "nov, Fs_nov = libfmp.c6.compute_novelty_spectrum(x, Fs=Fs, N=2048, H=512, gamma=100, M=10, norm=1)\n", "nov, Fs_nov = libfmp.c6.resample_signal(nov, Fs_in=Fs_nov, Fs_out=100)\n", "\n", "#Autocorrelation parameters\n", "N = 300 #corresponding to 3 seconds (Fs_nov = 100 Hz)\n", "H = 10\n", "\n", "A, T_coef, F_coef_lag = compute_autocorrelation_local(nov, Fs_nov, N, H, norm_sum=False)\n", "A_norm, T_coef, F_coef_lag = compute_autocorrelation_local(nov, Fs_nov, N, H)\n", "\n", "fig, ax = plt.subplots(3, 2, gridspec_kw={'width_ratios': [1, 0.05], \n", " 'height_ratios': [1, 2, 2]}, figsize=(8,7)) \n", "\n", "libfmp.b.plot_signal(nov, Fs_nov, ax=ax[0,0], color='k', title='Novelty function')\n", "ax[0,1].set_axis_off()\n", "\n", "libfmp.b.plot_matrix(A, ax=[ax[1,0], ax[1,1]], T_coef=T_coef, F_coef=F_coef_lag,\n", " title=r'Time-lag representation $\\mathcal{A}$', ylabel='Lag (seconds)', colorbar=True);\n", "\n", "libfmp.b.plot_matrix(A_norm, ax=[ax[2,0], ax[2,1]], T_coef=T_coef, F_coef=F_coef_lag,\n", " title=r'Time-lag representation $\\mathcal{A}$ (with sum normalization)', \n", " ylabel='Lag (seconds)', colorbar=True);\n", "plt.tight_layout()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Interpretation\n", "\n", "We now have a closer look at the **time–lag representation** and its interpretation continuing our Shostakovich example. The window $w$ used in this example is a rectangular window that has a length corresponding to $3~\\mathrm{sec}$ of the original audio recording. First, we consider the time index $n$ corresponding to the time instance $t=2~\\mathrm{sec}$. To compute $\\mathcal{A}(n,\\ell)$, one only considers the section of the novelty function $\\Delta$ between $0.5~\\mathrm{sec}$ and $3.5~\\mathrm{sec}$. The tempo of our Shostakovich recording is roughly $230~\\mathrm{BPM}$ in this section. In other words, the duration of the interval between two subsequent beats is roughly $s=0.26~\\mathrm{sec}$. Next, let us consider the lag parameter $\\ell$ that corresponds to a time shift of $s=0.26~\\mathrm{sec}$. Then, the novelty function in this section nicely correlates with its time-shifted version: the peaks of the section fall onto peaks of the section shifted by one beat period. The same holds when shifting the section by two, three or more beat periods. For example, this is the case for $s=0.78~\\mathrm{sec}$ (three beat periods). This period corresponds to a tempo of $77~\\mathrm{BPM}$, which is the tempo on the measure level. In contrast, when using a lag $\\ell$ that corresponds to half a beat period $s=0.13~\\mathrm{sec}$ (double tempo $461~\\mathrm{BPM}$), the peaks of the section and the peaks of the shifted section miss each other, thus resulting in a coefficient $\\mathcal{A}(n,\\ell)$ close to zero.\n", "\n", "" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "execution": { "iopub.execute_input": "2024-02-15T08:49:28.260344Z", "iopub.status.busy": "2024-02-15T08:49:28.260133Z", "iopub.status.idle": "2024-02-15T08:49:28.906926Z", "shell.execute_reply": "2024-02-15T08:49:28.906169Z" } }, "outputs": [ { "data": { "image/png": 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\n", 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\n", 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\n", 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\n", 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "def plot_signal_local_lag(x, t_x, local_lag, t_local_lag, lag, xlim=None, figsize=(8, 1.5), title=''):\n", " \"\"\"Visualize signal and local lag [FMP, Figure 6.14]\n", "\n", " Notebook: C6/C6S2_TempogramAutocorrelation.ipynb\n", "\n", " Args:\n", " x: Signal\n", " t_x: Time axis of x (given in seconds)\n", " local_lag: Local lag\n", " t_local_lag: Time axis of kernel (given in seconds)\n", " lag: Lag (given in seconds)\n", " xlim: Limits for x-axis (Default value = None)\n", " figsize: Figure size (Default value = (8, 1.5))\n", " title: Title of figure (Default value = '')\n", "\n", " Returns:\n", " fig: Matplotlib figure handle\n", " \"\"\"\n", " if xlim is None:\n", " xlim = [t_x[0], t_x[-1]]\n", " fig = plt.figure(figsize=figsize)\n", " plt.plot(t_x, x, 'k:', linewidth=0.5)\n", " plt.plot(t_local_lag, local_lag, 'k', linewidth=3.0)\n", " plt.plot(t_local_lag+lag, local_lag, 'r', linewidth=2)\n", " plt.title(title)\n", " plt.ylim([0, 1.1 * np.max(x)])\n", " plt.xlim(xlim)\n", " plt.tight_layout()\n", " return fig\n", " \n", "time_sec = np.array([2, 2, 2])\n", "lag_sec = np.array([0.13, 0.26, 0.78])\n", "coef_n = (time_sec * Fs_nov/H).astype(int)\n", "coef_k = (lag_sec * Fs_nov).astype(int)\n", "\n", "fig, ax, im = libfmp.b.plot_matrix(A, T_coef=T_coef, F_coef=F_coef_lag, figsize=(9,3),\n", " title=r'Time-lag representation $\\mathcal{A}$', ylabel='Lag (seconds)', colorbar=True);\n", "ax[0].plot(T_coef[coef_n], F_coef_lag[coef_k],'ro')\n", "\n", "L = len(nov)\n", "L_left = round(N/2)\n", "L_right = L_left\n", "nov_pad = np.concatenate( ( np.zeros(L_left), nov, np.zeros(L_right) ) )\n", "L_pad = len(nov_pad)\n", "win = np.ones(N)\n", "\n", "time_sec = np.array([2, 2, 2, 2])\n", "lag_sec = np.array([0, 0.13, 0.26, 0.78])\n", "t_nov = np.arange(nov.shape[0])/Fs_nov\n", "\n", "for i in range(len(time_sec)):\n", " t_0 = time_sec[i] * Fs_nov\n", " t_1 = t_0 + N\n", " nov_local = win*nov_pad[t_0:t_1]\n", " t_nov_local = (np.arange(t_0,t_1) - L_left)/Fs_nov\n", " lag = lag_sec[i]\n", " title=r'Shifted novelty function (t = %0.2f sec, $\\ell$ = %0.2f sec)'%(time_sec[i], lag)\n", " fig = plot_signal_local_lag(nov, t_nov, nov_local, t_nov_local, lag, title=title)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Autocorrelation Tempogram\n", "\n", "To obtain a time–tempo representation from the time–lag representation, one needs to convert the lag parameter into a tempo parameter. To this end, one requires the frame rate or time resolution of the novelty function. Suppose that each time frame corresponds to $r$ seconds, then a time lag of $\\ell$ (given in frames) corresponds to $\\ell\\cdot r$ seconds. Since a shift of $\\ell\\cdot r$ seconds corresponds to a rate of $1/(\\ell\\cdot r)~\\mathrm{Hz}$, one obtains the tempo\n", "\n", "\\begin{equation}\n", " \\tau = \\frac{60}{r\\cdot \\ell}~\\mathrm{BPM}.\n", "\\end{equation}\n", "\n", "As an example, let us assume that the feature rate of the novelty function $\\Delta$ is $F_\\mathrm{s}^\\Delta = 100~\\mathrm{Hz}$ and $r=0.01~\\mathrm{sec}$. In this case, the lag parameter $\\ell=10$ corresponds to $600~\\mathrm{BPM}$ and $\\ell=200$ corresponds to $30~\\mathrm{BPM}$. To obtain tempo values in a meaningful range, one uses in practice a maximum lag parameter $\\ell_\\mathrm{max}$ to specify the minimum tempo and a minimum lag parameter $\\ell_\\mathrm{min}$ to specify the maximum tempo. Note that when using a window length $N$, one needs to \n", "\n", "$$\n", " 1\\leq \\ell_\\mathrm{min} \\leq \\ell_\\mathrm{max} \\leq N-1\n", "$$\n", "\n", "Based on the conversion from lag to BPM, the lag axis can be interpreted as a tempo axis. This allows us to define the **autocorrelation tempogram** $\\mathcal{T}^\\mathrm{A}$ by setting\n", "\n", "\\begin{equation}\n", "\\mathcal{T}^\\mathrm{A}(n,\\tau) := \\mathcal{A}(n,\\ell)\n", "\\end{equation}\n", "\n", "for each tempo $\\tau=60/(r\\cdot\\ell)$ with $\\ell\\in[ \\ell_\\mathrm{min}: \\ell_\\mathrm{max}]$. Note that in this case, since the tempo values are reciprocal to the linearly sampled lag values, the tempo axis is sampled in a nonlinear fashion. To obtain a tempogram \n", "\n", "$$\n", "\\mathcal{T}^\\mathrm{A}: \\mathbb{Z} \\times \\Theta \\to \\mathbb{R}_{\\geq 0}\n", "$$ \n", "\n", "that is defined on the same tempo set $\\Theta$ as the Fourier tempogram $\\mathcal{T}^\\mathrm{F}$, one can use standard resampling and interpolation techniques applied to the tempo domain. The conversion from lag to tempo as well as the interpolation to obtain a linear tempo axis are illustrated by the following code cell continuing our Shostakovich example.\n", "\n", "" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "execution": { "iopub.execute_input": "2024-02-15T08:49:28.911564Z", "iopub.status.busy": "2024-02-15T08:49:28.910830Z", "iopub.status.idle": "2024-02-15T08:49:29.309657Z", "shell.execute_reply": "2024-02-15T08:49:29.308778Z" } }, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# @jit(nopython=True)\n", "def compute_tempogram_autocorr(x, Fs, N, H, norm_sum=False, Theta=np.arange(30, 601)):\n", " \"\"\"Compute autocorrelation-based tempogram\n", "\n", " Notebook: C6/C6S2_TempogramAutocorrelation.ipynb\n", "\n", " Args:\n", " x (np.ndarray): Input signal\n", " Fs (scalar): Sampling rate\n", " N (int): Window length\n", " H (int): Hop size\n", " norm_sum (bool): Normalizes by the number of summands in local autocorrelation (Default value = False)\n", " Theta (np.ndarray): Set of tempi (given in BPM) (Default value = np.arange(30, 601))\n", "\n", " Returns:\n", " tempogram (np.ndarray): Tempogram tempogram\n", " T_coef (np.ndarray): Time axis T_coef (seconds)\n", " F_coef_BPM (np.ndarray): Tempo axis F_coef_BPM (BPM)\n", " A_cut (np.ndarray): Time-lag representation A_cut (cut according to Theta)\n", " F_coef_lag_cut (np.ndarray): Lag axis F_coef_lag_cut\n", " \"\"\"\n", " tempo_min = Theta[0]\n", " tempo_max = Theta[-1]\n", " lag_min = int(np.ceil(Fs * 60 / tempo_max))\n", " lag_max = int(np.ceil(Fs * 60 / tempo_min))\n", " A, T_coef, F_coef_lag = compute_autocorrelation_local(x, Fs, N, H, norm_sum=norm_sum)\n", " A_cut = A[lag_min:lag_max+1, :]\n", " F_coef_lag_cut = F_coef_lag[lag_min:lag_max+1]\n", " F_coef_BPM_cut = 60 / F_coef_lag_cut\n", " F_coef_BPM = Theta\n", " tempogram = interp1d(F_coef_BPM_cut, A_cut, kind='linear',\n", " axis=0, fill_value='extrapolate')(F_coef_BPM)\n", " return tempogram, T_coef, F_coef_BPM, A_cut, F_coef_lag_cut\n", "\n", "Theta = np.arange(30, 601)\n", "tempogram, T_coef, F_coef, A, F_coef_lag = compute_tempogram_autocorr(nov, \n", " Fs_nov, N, H, norm_sum=False, Theta=Theta)\n", "\n", "fig, ax = plt.subplots(1, 3, gridspec_kw={'width_ratios': [1, 1, 1]}, figsize=(10,3)) \n", "\n", "libfmp.b.plot_matrix(A, T_coef=T_coef, F_coef=F_coef_lag, ax=[ax[0]],\n", " title='Lag axis', ylabel='Lag (seconds)', colorbar=True);\n", "lag_yticks = np.array([F_coef_lag[0], 0.5, 1.0, 1.5, F_coef_lag[-1]])\n", "ax[0].set_yticks(lag_yticks)\n", "\n", "libfmp.b.plot_matrix(A, T_coef=T_coef, F_coef=F_coef_lag, ax=[ax[1]],\n", " title='Lag converted to BPM', ylabel='Beat (BPM)', colorbar=True);\n", "ax[1].set_yticks(lag_yticks)\n", "ax[1].set_yticklabels(60/lag_yticks)\n", "\n", "libfmp.b.plot_matrix(tempogram, T_coef=T_coef, F_coef=F_coef, ax=[ax[2]],\n", " title='Linear BPM axis', ylabel='Beat (BPM)', colorbar=True);\n", "ax[2].set_yticks([F_coef[0], 120, 240, 360, 480, F_coef[-1]]);\n", "plt.tight_layout()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Tempo Harmonics and Subharmonics\n", "\n", "In the [FMP notebook on tempo and beat](../C6/C6S2_TempoBeat.html), introduces the notion of tempo octave, harmonic, and subharmonic. In the following, we illustrate these concepts by considering a click track of increasing tempo (raising from $170$ to $200~\\mathrm{BPM}$ over the course of $20~\\mathrm{sec}$). This example illustrates that an **autocorrelation tempogram** exhibits **tempo subharmonics**, while suppressing tempo harmonics. The reason for this behavior is that a high correlation of a local section of the novelty function with the section shifted by $\\ell$ samples also implies a high correlation with a section shifted by $k\\cdot\\ell$ lags for integers $k\\in\\mathbb{N}$. Assuming that $\\ell$ corresponds to tempo $\\tau$, the lag $k\\cdot\\ell$ corresponds to the subharmonic $\\tau/k$. Note that this behavior stands in contrast to the [**Fourier tempogram**](../C6/C6S2_TempogramFourier.html), which emphasizes **tempo harmonics**, but suppresses tempo subharmonics.\n", "\n", "\n", "\n", "" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "execution": { "iopub.execute_input": "2024-02-15T08:49:29.312463Z", "iopub.status.busy": "2024-02-15T08:49:29.312271Z", "iopub.status.idle": "2024-02-15T08:49:31.553878Z", "shell.execute_reply": "2024-02-15T08:49:31.553173Z" } }, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "fn_wav = os.path.join('..', 'data', 'C6', 'FMP_C6_F11_ClickTrack-BPM170-200.wav')\n", "Fs = 22050\n", "x, Fs = librosa.load(fn_wav, Fs) \n", "\n", "nov, Fs_nov = libfmp.c6.compute_novelty_spectrum(x, Fs=Fs, N=2048, H=512, gamma=100, M=10, norm=1)\n", "nov, Fs_nov = libfmp.c6.resample_signal(nov, Fs_in=Fs_nov, Fs_out=100)\n", "\n", "#Autocorrelation parameters\n", "N = 500 #corresponding to 5 seconds (Fs_nov = 100 Hz)\n", "H = 10\n", "Theta = np.arange(30, 601)\n", "tempogram_A, T_coef, F_coef, A, F_coef_lag = compute_tempogram_autocorr(nov, Fs_nov, N, H, \n", " norm_sum=False, Theta=Theta)\n", "\n", "fig, ax = plt.subplots(3, 2, gridspec_kw={'width_ratios': [1, 0.05], \n", " 'height_ratios': [1, 2, 2]}, figsize=(8,7)) \n", "libfmp.b.plot_signal(nov, Fs_nov, ax=ax[0,0], color='k', title='Novelty function')\n", "ax[0,1].set_axis_off()\n", "libfmp.b.plot_matrix(tempogram_A, T_coef=T_coef, F_coef=F_coef, ax=[ax[1,0], ax[1,1]], \n", " title='Autocorrelation tempogram', ylabel='Tempo (BPM)', colorbar=True);\n", "\n", "\n", "X, T_coef, F_coef_BPM = libfmp.c6.compute_tempogram_fourier(nov, Fs_nov, N=N, H=H, Theta=Theta)\n", "tempogram_F = np.abs(X)\n", "\n", "libfmp.b.plot_matrix(tempogram_F, T_coef=T_coef, F_coef=F_coef_BPM, ax=[ax[2,0], ax[2,1]], \n", " title='Fourier tempogram', ylabel='Tempo (BPM)', colorbar=True)\n", "plt.tight_layout()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "
\n", "Acknowledgment: This notebook was created by Meinard Müller and Angel Villar-Corrales.\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\"
" ] } ], "metadata": { "anaconda-cloud": {}, "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.8.16" } }, "nbformat": 4, "nbformat_minor": 1 }