Michaël Defferrard, Kirell Benzi, Pierre Vandergheynst, Xavier Bresson, EPFL LTS2.

Usage

Go through the paper to understand what the data is about.
Download some datasets from https://github.com/mdeff/fma.
Uncompress the archives, e.g. with unzip fma_small.zip.
Load and play with the data in this notebook.



In [ ]:

    
%matplotlib inline

import os

import IPython.display as ipd
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import sklearn as skl
import sklearn.utils, sklearn.preprocessing, sklearn.decomposition, sklearn.svm
import librosa
import librosa.display

import utils

plt.rcParams['figure.figsize'] = (17, 5)



In [ ]:

    
# Directory where mp3 are stored.
AUDIO_DIR = os.environ.get('AUDIO_DIR')

# Load metadata and features.
tracks = utils.load('data/fma_metadata/tracks.csv')
genres = utils.load('data/fma_metadata/genres.csv')
features = utils.load('data/fma_metadata/features.csv')
echonest = utils.load('data/fma_metadata/echonest.csv')

np.testing.assert_array_equal(features.index, tracks.index)
assert echonest.index.isin(tracks.index).all()

tracks.shape, genres.shape, features.shape, echonest.shape

1 Metadata

The metadata table, a CSV file in the fma_metadata.zip archive, is composed of many colums:

The index is the ID of the song, taken from the website, used as the name of the audio file.
Per-track, per-album and per-artist metadata from the Free Music Archive website.
Two columns to indicate the subset (small, medium, large) and the split (training, validation, test).



In [ ]:

    
ipd.display(tracks['track'].head())
ipd.display(tracks['album'].head())
ipd.display(tracks['artist'].head())
ipd.display(tracks['set'].head())

1.1 Subsets

The small and medium subsets can be selected with the below code.



In [ ]:

    
small = tracks[tracks['set', 'subset'] <= 'small']
small.shape



In [ ]:

    
medium = tracks[tracks['set', 'subset'] <= 'medium']
medium.shape

2 Genres

The genre hierarchy is stored in genres.csv and distributed in fma_metadata.zip.



In [ ]:

    
print('{} top-level genres'.format(len(genres['top_level'].unique())))
genres.loc[genres['top_level'].unique()].sort_values('#tracks', ascending=False)



In [ ]:

    
genres.sort_values('#tracks').head(10)

3 Features

Features extracted from the audio for all tracks.
For some tracks, data colected from the Echonest API.



In [ ]:

    
print('{1} features for {0} tracks'.format(*features.shape))
columns = ['mfcc', 'chroma_cens', 'tonnetz', 'spectral_contrast']
columns.append(['spectral_centroid', 'spectral_bandwidth', 'spectral_rolloff'])
columns.append(['rmse', 'zcr'])
for column in columns:
    ipd.display(features[column].head().style.format('{:.2f}'))

3.1 Echonest features



In [ ]:

    
print('{1} features for {0} tracks'.format(*echonest.shape))
ipd.display(echonest['echonest', 'metadata'].head())
ipd.display(echonest['echonest', 'audio_features'].head())
ipd.display(echonest['echonest', 'social_features'].head())
ipd.display(echonest['echonest', 'ranks'].head())



In [ ]:

    
ipd.display(echonest['echonest', 'temporal_features'].head())
x = echonest.loc[2, ('echonest', 'temporal_features')]
plt.plot(x);

3.2 Features like MFCCs are discriminant



In [ ]:

    
small = tracks['set', 'subset'] <= 'small'
genre1 = tracks['track', 'genre_top'] == 'Instrumental'
genre2 = tracks['track', 'genre_top'] == 'Hip-Hop'

X = features.loc[small & (genre1 | genre2), 'mfcc']
X = skl.decomposition.PCA(n_components=2).fit_transform(X)

y = tracks.loc[small & (genre1 | genre2), ('track', 'genre_top')]
y = skl.preprocessing.LabelEncoder().fit_transform(y)

plt.scatter(X[:,0], X[:,1], c=y, cmap='RdBu', alpha=0.5)
X.shape, y.shape

4 Audio

You can load the waveform and listen to audio in the notebook itself.



In [ ]:

    
filename = utils.get_audio_path(AUDIO_DIR, 2)
print('File: {}'.format(filename))

x, sr = librosa.load(filename, sr=None, mono=True)
print('Duration: {:.2f}s, {} samples'.format(x.shape[-1] / sr, x.size))

start, end = 7, 17
ipd.Audio(data=x[start*sr:end*sr], rate=sr)

And use librosa to compute spectrograms and audio features.



In [ ]:

    
librosa.display.waveplot(x, sr, alpha=0.5);
plt.vlines([start, end], -1, 1)

start = len(x) // 2
plt.figure()
plt.plot(x[start:start+2000])
plt.ylim((-1, 1));



In [ ]:

    
stft = np.abs(librosa.stft(x, n_fft=2048, hop_length=512))
mel = librosa.feature.melspectrogram(sr=sr, S=stft**2)
log_mel = librosa.logamplitude(mel)

librosa.display.specshow(log_mel, sr=sr, hop_length=512, x_axis='time', y_axis='mel');



In [ ]:

    
mfcc = librosa.feature.mfcc(S=librosa.power_to_db(mel), n_mfcc=20)
mfcc = skl.preprocessing.StandardScaler().fit_transform(mfcc)
librosa.display.specshow(mfcc, sr=sr, x_axis='time');

5 Genre classification

5.1 From features



In [ ]:

    
small = tracks['set', 'subset'] <= 'small'

train = tracks['set', 'split'] == 'training'
val = tracks['set', 'split'] == 'validation'
test = tracks['set', 'split'] == 'test'

y_train = tracks.loc[small & train, ('track', 'genre_top')]
y_test = tracks.loc[small & test, ('track', 'genre_top')]
X_train = features.loc[small & train, 'mfcc']
X_test = features.loc[small & test, 'mfcc']

print('{} training examples, {} testing examples'.format(y_train.size, y_test.size))
print('{} features, {} classes'.format(X_train.shape[1], np.unique(y_train).size))



In [ ]:

    
# Be sure training samples are shuffled.
X_train, y_train = skl.utils.shuffle(X_train, y_train, random_state=42)

# Standardize features by removing the mean and scaling to unit variance.
scaler = skl.preprocessing.StandardScaler(copy=False)
scaler.fit_transform(X_train)
scaler.transform(X_test)

# Support vector classification.
clf = skl.svm.SVC()
clf.fit(X_train, y_train)
score = clf.score(X_test, y_test)
print('Accuracy: {:.2%}'.format(score))

FMA: A Dataset For Music Analysis