FMP AudioLabs

Symbolic Format: CSV

Following Section 1.2 of [Müller, FMP, Springer 2015], we introduce in this notebook at a simple symbolic music representation encoded in some comma-separated values (CSV) format.

CSV Format

Recall that a piano-roll representation yields a visualization of note events, where each note is encoded by a start, duration, and pitch parameter. Further parameters could encode a note's velocity, articulation, or instrumentation. In the following, we introduce a simple format stored in a comma-separated values (CSV) file. Each note event corresponds to a line encoding a data record consisting of the four parameters start, duration, pitch, velocity, and label. The first four parameters are real numbers and encode the start (given in seconds or measures), duration (given in seconds or measures), pitch (encoded as MIDI note number), and velocity (in the range between 0.0 and 1.0) of a note event. The last parameter label is a string that my encode further information such as the instrument (e.g. violin, trumpet, flute), the voice (e.g. soprano, alto, tenor and bass), or some staff information (e.g. left/right hand for a piano score). In the following, we use the first five measures of Symphony No. 5 by Ludwig van Beethoven in a piano reduced version as a running example.


A CSV file of a piano-roll representation of this score looks as follows:

In [1]:
import os
import numpy as np
from matplotlib import pyplot as plt
from matplotlib import patches
import pandas as pd
import IPython.display as ipd

%matplotlib inline

fn = os.path.join('..', 'data', 'C1', 'FMP_C1_F01_Beethoven_FateMotive_Sibelius.csv')

with open(fn, 'r', encoding='utf-8') as file:
    csv_str =

0.25;0.23;67.0;1.0;"Right Hand"
0.25;0.23;55.0;1.0;"Right Hand"
0.25;0.23;43.0;1.0;"Left Hand"
0.5;0.23;67.0;1.0;"Right Hand"
0.5;0.23;55.0;1.0;"Right Hand"
0.5;0.23;43.0;1.0;"Left Hand"
0.75;0.23;67.0;1.0;"Right Hand"
0.75;0.23;55.0;1.0;"Right Hand"
0.75;0.23;43.0;1.0;"Left Hand"
1.0;0.97;63.0;1.0;"Right Hand"
1.0;0.97;51.0;1.0;"Right Hand"
1.0;0.97;39.0;1.0;"Left Hand"
2.25;0.23;65.0;1.0;"Right Hand"
2.25;0.23;53.0;1.0;"Right Hand"
2.25;0.23;41.0;1.0;"Left Hand"
2.5;0.23;65.0;1.0;"Right Hand"
2.5;0.23;53.0;1.0;"Right Hand"
2.5;0.23;41.0;1.0;"Left Hand"
2.75;0.23;65.0;1.0;"Right Hand"
2.75;0.23;53.0;1.0;"Right Hand"
2.75;0.23;41.0;1.0;"Left Hand"
3.0;1.97;62.0;1.0;"Right Hand"
3.0;1.97;50.0;1.0;"Left Hand"
3.0;1.97;38.0;1.0;"Left Hand"

Python Library pandas

The Python library pandas provides easy-to-use data structures and data analysis tools. In particular, it can be used to easily read and modify CSV files. When reading a CSV file, the content is stored in a two-dimensional tabular data structure with labeled axes (rows and columns) called DataFrame. This data structure can be accessed using attributes, see the documentation for pandas.DataFrame. We apply pandas to our previous example and print the first four rows.

In [2]:
df = pd.read_csv(fn, sep=';')
   Start  Duration  Pitch  Velocity       Label
0   0.25      0.23   67.0       1.0  Right Hand
1   0.25      0.23   55.0       1.0  Right Hand
2   0.25      0.23   43.0       1.0   Left Hand
3   0.50      0.23   67.0       1.0  Right Hand
4   0.50      0.23   55.0       1.0  Right Hand
5   0.50      0.23   43.0       1.0   Left Hand

The pandas-library allows for rendering a DataFrame as an HTML table as shown in the next example.

In [3]:
html = df.loc[0:5,:].to_html(index=False)
Start Duration Pitch Velocity Label
0.25 0.23 67.0 1.0 Right Hand
0.25 0.23 55.0 1.0 Right Hand
0.25 0.23 43.0 1.0 Left Hand
0.50 0.23 67.0 1.0 Right Hand
0.50 0.23 55.0 1.0 Right Hand
0.50 0.23 43.0 1.0 Left Hand

Piano-Roll Visualization

The note events stored in the CSV file can be visualized using a piano-roll representation. The following functions illustrates how this may be done. Note that the color of a rectangle is used to indicated the different labels associated to note events.

In [4]:
import sys
import libfmp.b

def csv_to_list(csv):
    """Convert a csv score file to a list of note events

    Notebook: C1/C1S2_CSV.ipynb

        csv (str or pd.DataFrame): Either a path to a csv file or a data frame

        score (list): A list of note events where each note is specified as
            ``[start, duration, pitch, velocity, label]``

    if isinstance(csv, str):
        df = libfmp.b.read_csv(csv)
    elif isinstance(csv, pd.DataFrame):
        df = csv
        raise RuntimeError('csv must be a path to a csv file or pd.DataFrame')

    score = []
    for i, (start, duration, pitch, velocity, label) in df.iterrows():
        score.append([start, duration, pitch, velocity, label])
    return score

def visualize_piano_roll(score, xlabel='Time (seconds)', ylabel='Pitch', colors='FMP_1', velocity_alpha=False,
                         figsize=(12, 4), ax=None, dpi=72):
    """Plot a pianoroll visualization

    Notebook: C1/C1S2_CSV.ipynb

        score: List of note events
        xlabel: Label for x axis (Default value = 'Time (seconds)')
        ylabel: Label for y axis (Default value = 'Pitch')
        colors: Several options: 1. string of FMP_COLORMAPS, 2. string of matplotlib colormap,
            3. list or np.ndarray of matplotlib color specifications,
            4. dict that assigns labels  to colors (Default value = 'FMP_1')
        velocity_alpha: Use the velocity value for the alpha value of the corresponding rectangle
            (Default value = False)
        figsize: Width, height in inches (Default value = (12)
        ax: The Axes instance to plot on (Default value = None)
        dpi: Dots per inch (Default value = 72)

        fig: The created matplotlib figure or None if ax was given.
        ax: The used axes
    fig = None
    if ax is None:
        fig = plt.figure(figsize=figsize, dpi=dpi)
        ax = plt.subplot(1, 1, 1)

    labels_set = sorted(set([note[4] for note in score]))
    colors = libfmp.b.color_argument_to_dict(colors, labels_set)

    pitch_min = min(note[2] for note in score)
    pitch_max = max(note[2] for note in score)
    time_min = min(note[0] for note in score)
    time_max = max(note[0] + note[1] for note in score)

    for start, duration, pitch, velocity, label in score:
        if velocity_alpha is False:
            velocity = None
        rect = patches.Rectangle((start, pitch - 0.5), duration, 1, linewidth=1,
                                 edgecolor='k', facecolor=colors[label], alpha=velocity)

    ax.set_ylim([pitch_min - 1.5, pitch_max + 1.5])
    ax.set_xlim([min(time_min, 0), time_max + 0.5])
    ax.legend([patches.Patch(linewidth=1, edgecolor='k', facecolor=colors[key]) for key in labels_set],
              labels_set, loc='upper right', framealpha=1)

    if fig is not None:

    return fig, ax

score = csv_to_list(df)
visualize_piano_roll(score, colors=['red','blue'], figsize=(8, 3));

Piano-Roll Representation for Orchestral Version

The next example shows a piano-roll representation for a full orchestral score of the first 21 measures of Beethoven's Fifth, see the notebook on sheet music representations. Note that note events that overlap in the time–pitch domain are painted over by the visualization function in a greedy fashion.


In [5]:
fn = os.path.join('..', 'data', 'C1', 'FMP_C1_F10_Beethoven_Fifth-MM1-21_Sibelius-Orchestra.csv')

df = pd.read_csv(fn, sep=';')
score_list = csv_to_list(df)
visualize_piano_roll(score_list, figsize=(10, 7), colors='gist_rainbow');

Piano Roll Representation for Fugue

The next example shows a piano-roll representation of the beginning of the four-voice Fugue BWV 846 in C major by Johann Sebastian Bach. The four different voices are labeled with 'Soprano', 'Alto', 'Tenor', and 'Basso'.


In [6]:
fn = os.path.join('..', 'data', 'C1', 'FMP_C1_F12_Bach_BWV846_Sibelius-Tracks.csv')

df = pd.read_csv(fn, sep=';')
score_list = csv_to_list(df)
visualize_piano_roll(score_list, figsize=(8, 3));

#output_path_filename = os.path.join('..', 'output', 'C1', 'FMP_C1_F12_Bach_BWV846_PianoRoll.png')

Further Notes

The functions to convert a csv file to a list of note events as well as the pianoroll visualiztion function have been included into libfmp. In the following code cell, we call those libfmp functions:

In [7]:
import sys
import libfmp.c1

fn = os.path.join('..', 'data', 'C1', 'FMP_C1_F01_Beethoven_FateMotive_Sibelius.csv')
score = libfmp.c1.csv_to_list(fn)
libfmp.c1.visualize_piano_roll(score, figsize=(8, 3));
Acknowledgment: This notebook was created by Frank Zalkow and Meinard Müller.
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