def plot_poly_features(number):
example_mp3, sr, song_name = load_music.load_song(number)
S = np.abs(librosa.stft(example_mp3))
p0 = librosa.feature.poly_features(S=S, order=0)
p1 = librosa.feature.poly_features(S=S, order=1)
p2 = librosa.feature.poly_features(S=S, order=2)
fig, ax = plt.subplots(nrows=4, sharex=True, figsize=(8, 8))
times = librosa.times_like(p0)
ax[0].plot(times, p0[0], label='order=0', alpha=0.8)
ax[0].plot(times, p1[1], label='order=1', alpha=0.8)
ax[0].plot(times, p2[2], label='order=2', alpha=0.8)
ax[0].legend()
ax[0].label_outer()
ax[0].set(ylabel='Constant term ')
ax[1].plot(times, p1[0], label='order=1', alpha=0.8)
ax[1].plot(times, p2[1], label='order=2', alpha=0.8)
ax[1].set(ylabel='Linear term')
ax[1].label_outer()
ax[1].legend()
ax[2].plot(times, p2[0], label='order=2', alpha=0.8)
ax[2].set(ylabel='Quadratic term')
ax[2].legend()
librosa.display.specshow(librosa.amplitude_to_db(S, ref=np.max),
y_axis='log',
x_axis='time',
ax=ax[3])
fig.suptitle('Poly features on ' + song_name, fontsize=8)
plt.show()
def plot_spectral_rolfoff(number):
example_mp3, sr, song_name = load_music.load_song(number)
fig, ax = plt.subplots()
rolloff = librosa.feature.spectral_rolloff(y=example_mp3,
sr=sr,
roll_percent=0.99)
rolloff_min = librosa.feature.spectral_rolloff(y=example_mp3,
sr=sr,
roll_percent=0.01)
S, phase = librosa.magphase(librosa.stft(example_mp3))
librosa.display.specshow(librosa.amplitude_to_db(S, ref=np.max),
y_axis='log',
x_axis='time',
ax=ax)
ax.plot(librosa.times_like(rolloff),
rolloff[0],
label='Roll-off frequency (0.99)')
ax.plot(librosa.times_like(rolloff),
rolloff_min[0],
color='w',
label='Roll-off frequency (0.01)')
ax.legend(loc='lower right')
ax.set(title='log Power spectrogram')
fig.suptitle('Spectral rolfoff on ' + song_name, fontsize=8)
plt.show()
def plot_spectral_flatness(number):
example_mp3, sr, song_name = load_music.load_song(number)
flatness = librosa.feature.spectral_flatness(y=example_mp3)
plt.plot(range(0, len(flatness[0])), flatness[0])
plt.title('Spectral Flatness on ' + song_name)
plt.show()
def plot_spectral_bandwidth(number):
example_mp3, sr, song_name = load_music.load_song(number)
fig, ax = plt.subplots(nrows=2, sharex=True)
spec_bw = librosa.feature.spectral_bandwidth(y=example_mp3, sr=sr)
S, phase = librosa.magphase(librosa.stft(y=example_mp3))
times = librosa.times_like(spec_bw)
centroid = librosa.feature.spectral_centroid(S=S)
ax[0].semilogy(times, spec_bw[0], label='Spectral bandwidth')
ax[0].set(ylabel='Hz', xticks=[], xlim=[times.min(), times.max()])
ax[0].legend()
ax[0].label_outer()
librosa.display.specshow(librosa.amplitude_to_db(S, ref=np.max),
y_axis='log',
x_axis='time',
ax=ax[1])
ax[1].set(title='log Power spectrogram')
ax[1].fill_between(times,
centroid[0] - spec_bw[0],
centroid[0] + spec_bw[0],
alpha=0.5,
label='Centroid +- bandwidth')
ax[1].plot(times, centroid[0], label='Spectral centroid', color='w')
ax[1].legend(loc='lower right')
fig.suptitle('Spectral bandwidth on ' + song_name, fontsize=7)
plt.show()
def plot_zero_crossing_rate(number):
example_mp3, sr, song_name = load_music.load_song(number)
fig, ax = plt.subplots()
zcr = librosa.feature.zero_crossing_rate(example_mp3)
plt.plot(range(0, len(zcr[0])), zcr[0])
fig.suptitle('ZCR on ' + song_name, fontsize=8)
plt.show()
def plot_mfcc(number):
example_mp3, sr, song_name = load_music.load_song(number)
example_mp3 = example_mp3[:220500]
fig, ax = plt.subplots()
mfccs = librosa.feature.mfcc(example_mp3, sr)
print(mfccs.shape)
img = librosa.display.specshow(mfccs, x_axis='time', sr=sr)
fig.colorbar(img, ax=ax)
fig.suptitle('MFCC on' + ' ' + song_name, fontsize=8)
fig.tight_layout()
plt.show()
def plot_mel_frequency_spectogram(number):
example_mp3, sr, song_name = load_music.load_song(number)
fig, ax = plt.subplots()
S = librosa.feature.melspectrogram(example_mp3, sr)
S_dB = librosa.power_to_db(S, ref=np.max)
img = librosa.display.specshow(S_dB, x_axis='time', y_axis='mel', sr=sr)
fig.colorbar(img, ax=ax, format='%+2.0f dB')
fig.suptitle('Mel-frequency spectogram on' + ' ' + song_name, fontsize=8)
fig.tight_layout()
plt.show()
def plot_chroma_cens(number):
example_mp3, sr, song_name = load_music.load_song(number)
chroma_cens = librosa.feature.chroma_cens(example_mp3, sr)
fig, ax = plt.subplots()
img = librosa.display.specshow(chroma_cens,
y_axis='chroma',
x_axis='time',
ax=ax)
fig.suptitle("Chroma_CQ on" + " " + song_name, fontsize=8)
fig.colorbar(img, ax=ax)
fig.tight_layout()
plt.show()
def plot_spectral_centroid(number):
example_mp3, sr, song_name = load_music.load_song(number)
cent = librosa.feature.spectral_centroid(example_mp3, sr)
S, phase = librosa.magphase(librosa.stft(y=example_mp3))
fig, ax = plt.subplots()
times = librosa.times_like(cent)
librosa.display.specshow(librosa.amplitude_to_db(S, ref=np.max),
y_axis='log',
x_axis='time',
ax=ax)
ax.plot(times, cent.T, label='Spectral centroid', color='w')
ax.legend(loc='upper right')
fig.suptitle('log power spectogram on' + ' ' + song_name, fontsize=8)
plt.show()
def plot_rms(number):
example_mp3, sr, song_name = load_music.load_song(number)
fig, ax = plt.subplots(nrows=2, sharex=True)
S, phase = librosa.magphase(librosa.stft(example_mp3))
rms = librosa.feature.rms(S=S)
times = librosa.times_like(rms)
ax[0].semilogy(times, rms[0], label='RMS Energy')
ax[0].set(xticks=[])
ax[0].legend()
ax[0].label_outer()
librosa.display.specshow(librosa.amplitude_to_db(S, ref=np.max),
y_axis='log',
x_axis='time',
ax=ax[1])
ax[1].set(title='loga Power spectrogram')
fig.suptitle('RMS on' + ' ' + song_name, fontsize=8)
plt.show()
def plot_spectral_contrast(number):
example_mp3, sr, song_name = load_music.load_song(number)
fig, ax = plt.subplots(nrows=2, sharex=True)
S = np.abs(librosa.stft(example_mp3))
contrast = librosa.feature.spectral_contrast(S=S, sr=sr)
img1 = librosa.display.specshow(librosa.amplitude_to_db(S, ref=np.max),
y_axis='log',
x_axis='time',
ax=ax[0])
fig.colorbar(img1, ax=[ax[0]], format='%+2.0f dB')
ax[0].set(title='Power spectrogram')
ax[0].label_outer()
img2 = librosa.display.specshow(contrast, x_axis='time', ax=ax[1])
fig.colorbar(img2, ax=[ax[1]])
fig.suptitle('Spectral contras on ' + song_name, fontsize=8)
ax[1].set(ylabel='Frequency bands', title='Spectral contrast')
plt.show()
def plot_chroma_stft_cqt(number):
example_mp3, sr, song_name = load_music.load_song(number)
chroma_cqt = librosa.feature.chroma_cqt(example_mp3, sr)
chroma_stft = librosa.feature.chroma_stft(example_mp3, sr)
fig, ax = plt.subplots(nrows=2, sharex=True, sharey=True)
librosa.display.specshow(chroma_stft,
y_axis='chroma',
x_axis='time',
ax=ax[0])
ax[0].set(title='chroma_stft')
ax[0].label_outer()
fig.suptitle(song_name)
img = librosa.display.specshow(chroma_cqt,
y_axis='chroma',
x_axis='time',
ax=ax[1])
ax[1].set(title='chroma_cqt')
fig.colorbar(img, ax=ax)
plt.show()
def plot_fourier_tempogram(number):
example_mp3, sr, song_name = load_music.load_song(number)
hop_length = 512
oenv = librosa.onset.onset_strength(y=example_mp3,
sr=sr,
hop_length=hop_length)
tempogram = librosa.feature.fourier_tempogram(onset_envelope=oenv,
sr=sr,
hop_length=hop_length)
# Compute the auto-correlation tempogram, unnormalized to make comparison easier
ac_tempogram = librosa.feature.tempogram(onset_envelope=oenv,
sr=sr,
hop_length=hop_length,
norm=None)
fig, ax = plt.subplots(nrows=3, sharex=True)
ax[0].plot(librosa.times_like(oenv), oenv, label='Onset strength')
ax[0].legend(frameon=True)
ax[0].label_outer()
librosa.display.specshow(np.abs(tempogram),
sr=sr,
hop_length=hop_length,
x_axis='time',
y_axis='fourier_tempo',
cmap='magma',
ax=ax[1])
ax[1].set(title='Fourier tempogram')
ax[1].label_outer()
librosa.display.specshow(ac_tempogram,
sr=sr,
hop_length=hop_length,
x_axis='time',
y_axis='tempo',
cmap='magma',
ax=ax[2])
ax[2].set(title='Autocorrelation tempogram')
fig.suptitle('Fourier Tempogram on ' + song_name, fontsize=8)
plt.show()
def plot_tonnetz(number):
example_mp3, sr, song_name = load_music.load_song(number)
harmonic = librosa.effects.harmonic(example_mp3)
tonnetz = librosa.feature.tonnetz(harmonic, sr)
fig, ax = plt.subplots(nrows=2, sharex=True)
img1 = librosa.display.specshow(tonnetz,
y_axis='tonnetz',
x_axis='time',
ax=ax[0])
ax[0].set(title='Tonal Centroids (Tonnetz)')
ax[0].label_outer()
img2 = librosa.display.specshow(librosa.feature.chroma_cqt(harmonic,
sr=sr),
y_axis='chroma',
x_axis='time',
ax=ax[1])
ax[1].set(title='Chroma')
fig.colorbar(img1, ax=[ax[0]])
fig.colorbar(img2, ax=[ax[1]])
fig.suptitle('Tonnetz on ' + song_name, fontsize=8)
plt.show()
def plot_tempogram(number):
example_mp3, sr, song_name = load_music.load_song(number)
hop_length = 512
oenv = librosa.onset.onset_strength(y=example_mp3,
sr=sr,
hop_length=hop_length)
tempogram = librosa.feature.tempogram(onset_envelope=oenv,
sr=sr,
hop_length=hop_length)
# Compute global onset autocorrelation
ac_global = librosa.autocorrelate(oenv, max_size=tempogram.shape[0])
ac_global = librosa.util.normalize(ac_global)
# Estimate the global tempo for display purposes
tempo = librosa.beat.tempo(onset_envelope=oenv,
sr=sr,
hop_length=hop_length)[0]
fig, ax = plt.subplots(nrows=4, figsize=(10, 10))
times = librosa.times_like(oenv, sr=sr, hop_length=hop_length)
ax[0].plot(times, oenv, label='Onset strength')
ax[0].label_outer()
ax[0].legend(frameon=True)
librosa.display.specshow(tempogram,
sr=sr,
hop_length=hop_length,
x_axis='time',
y_axis='tempo',
cmap='magma',
ax=ax[1])
ax[1].axhline(tempo,
color='w',
linestyle='--',
alpha=1,
label='Estimated tempo={:g}'.format(tempo))
ax[1].legend(loc='upper right')
ax[1].set(title='Tempogram')
x = np.linspace(0,
tempogram.shape[0] * float(hop_length) / sr,
num=tempogram.shape[0])
ax[2].plot(x,
np.mean(tempogram, axis=1),
label='Mean local autocorrelation')
ax[2].plot(x, ac_global, '--', alpha=0.75, label='Global autocorrelation')
ax[2].set(xlabel='Lag (seconds)')
ax[2].legend(frameon=True)
freqs = librosa.tempo_frequencies(tempogram.shape[0],
hop_length=hop_length,
sr=sr)
ax[3].semilogx(freqs[1:],
np.mean(tempogram[1:], axis=1),
label='Mean local autocorrelation',
basex=2)
ax[3].semilogx(freqs[1:],
ac_global[1:],
'--',
alpha=0.75,
label='Global autocorrelation',
basex=2)
ax[3].axvline(tempo,
color='black',
linestyle='--',
alpha=.8,
label='Estimated tempo={:g}'.format(tempo))
ax[3].legend(frameon=True)
ax[3].set(xlabel='BPM')
ax[3].grid(True)
fig.suptitle('Tempogram on ' + song_name, fontsize=8)
plt.show()