def compute_features(audio, y_harmonic):
"""Computes the HPCP and MFCC features.
Parameters
----------
audio: np.array(N)
Audio samples of the given input.
y_harmonic: np.array(N)
Harmonic part of the audio signal, in samples.
Returns
-------
mfcc: np.array(N, msaf.Anal.mfcc_coeff)
Mel-frequency Cepstral Coefficients.
hpcp: np.array(N, 12)
Pitch Class Profiles.
tonnetz: np.array(N, 6)
Tonal Centroid features.
cqt: np.array(N, msaf.Anal.cqt_bins)
Constant-Q log-scale features.
tempogram: np.array(N, 192)
Tempogram features.
"""
logging.info("Computing Spectrogram...")
S = librosa.feature.melspectrogram(audio,
sr=msaf.Anal.sample_rate,
n_fft=msaf.Anal.frame_size,
hop_length=msaf.Anal.hop_size,
n_mels=msaf.Anal.n_mels)
logging.info("Computing Constant-Q...")
cqt = librosa.logamplitude(np.abs(
librosa.cqt(audio,
sr=msaf.Anal.sample_rate,
hop_length=msaf.Anal.hop_size,
n_bins=msaf.Anal.cqt_bins,
real=False)) ** 2,
ref_power=np.max).T
logging.info("Computing MFCCs...")
log_S = librosa.logamplitude(S, ref_power=np.max)
mfcc = librosa.feature.mfcc(S=log_S, n_mfcc=msaf.Anal.mfcc_coeff).T
logging.info("Computing HPCPs...")
hpcp = librosa.feature.chroma_cqt(y=y_harmonic,
sr=msaf.Anal.sample_rate,
hop_length=msaf.Anal.hop_size,
n_octaves=msaf.Anal.n_octaves,
fmin=msaf.Anal.f_min).T
logging.info("Computing Tonnetz...")
tonnetz = utils.chroma_to_tonnetz(hpcp)
logging.info("Computing Tempogram...")
tempogram = librosa.feature.tempogram(audio,
sr=msaf.Anal.sample_rate,
hop_length=msaf.Anal.hop_size,
win_length=192).T
return mfcc, hpcp, tonnetz, cqt, tempogram
def compute_features(audio, y_harmonic):
"""Computes the HPCP and MFCC features.
Parameters
----------
audio: np.array(N)
Audio samples of the given input.
y_harmonic: np.array(N)
Harmonic part of the audio signal, in samples.
Returns
-------
mfcc: np.array(N, msaf.Anal.mfcc_coeff)
Mel-frequency Cepstral Coefficients.
hpcp: np.array(N, 12)
Pitch Class Profiles.
tonnetz: np.array(N, 6)
Tonal Centroid features.
cqt: np.array(N, msaf.Anal.cqt_bins)
Constant-Q log-scale features.
"""
logging.info("Computing Spectrogram...")
S = librosa.feature.melspectrogram(audio,
sr=msaf.Anal.sample_rate,
n_fft=msaf.Anal.frame_size,
hop_length=msaf.Anal.hop_size,
n_mels=msaf.Anal.n_mels)
logging.info("Computing Constant-Q...")
cqt = librosa.logamplitude(np.abs(
librosa.cqt(audio,
sr=msaf.Anal.sample_rate,
hop_length=msaf.Anal.hop_size,
n_bins=msaf.Anal.cqt_bins,
real=False))**2,
ref_power=np.max).T
logging.info("Computing MFCCs...")
log_S = librosa.logamplitude(S, ref_power=np.max)
mfcc = librosa.feature.mfcc(S=log_S, n_mfcc=msaf.Anal.mfcc_coeff).T
logging.info("Computing HPCPs...")
hpcp = librosa.feature.chroma_cqt(y=y_harmonic,
sr=msaf.Anal.sample_rate,
hop_length=msaf.Anal.hop_size,
n_octaves=msaf.Anal.n_octaves,
fmin=msaf.Anal.f_min).T
logging.info("Computing Tonnetz...")
tonnetz = utils.chroma_to_tonnetz(hpcp)
return mfcc, hpcp, tonnetz, cqt
def compute_features(audio, y_harmonic):
"""Computes the HPCP and MFCC features.
Parameters
----------
audio: np.array(N)
Audio samples of the given input.
y_harmonic: np.array(N)
Harmonic part of the audio signal, in samples.
Returns
-------
mfcc: np.array(N, msaf.Anal.mfcc_coeff)
Mel-frequency Cepstral Coefficients.
hpcp: np.array(N, 12)
Pitch Class Profiles.
tonnetz: np.array(N, 6)
Tonal Centroid features.
"""
logging.info("Computing Spectrogram...")
S = librosa.feature.melspectrogram(audio,
sr=22050,
n_fft=msaf.Anal.frame_size,
hop_length=msaf.Anal.hop_size,
n_mels=msaf.Anal.n_mels)
logging.info("Computing MFCCs...")
log_S = librosa.logamplitude(S, ref_power=np.max)
mfcc = librosa.feature.mfcc(S=log_S, n_mfcc=msaf.Anal.mfcc_coeff).T
logging.info("Computing HPCPs...")
hpcp = librosa.feature.chroma_cqt(y=y_harmonic,
sr=22050,
hop_length=msaf.Anal.hop_size).T
#plt.imshow(hpcp.T, interpolation="nearest", aspect="auto"); plt.show()
logging.info("Computing Tonnetz...")
tonnetz = utils.chroma_to_tonnetz(hpcp)
return mfcc, hpcp, tonnetz
def compute_features(audio, beats=None):
"""Computes the HPCP and MFCC beat-synchronous features given a set
of beats (beats)."""
beatsync_str = ""
if beats is not None:
beatsync_str = "Beat-synchronous "
MFCC = STFTFeature(msaf.Anal.frame_size, msaf.Anal.hop_size,
msaf.Anal.window_type,
ES.MFCC(numberCoefficients=msaf.Anal.mfcc_coeff),
msaf.Anal.sample_rate, beats)
HPCP = STFTFeature(msaf.Anal.frame_size, msaf.Anal.hop_size,
msaf.Anal.window_type, ES.HPCP(), msaf.Anal.sample_rate,
beats)
logging.info("Computing %sMFCCs..." % beatsync_str)
mfcc = MFCC.compute_features(audio)
logging.info("Computing %sHPCPs..." % beatsync_str)
hpcp = HPCP.compute_features(audio)
#plt.imshow(hpcp.T, interpolation="nearest", aspect="auto"); plt.show()
logging.info("Computing %sTonnetz..." % beatsync_str)
tonnetz = utils.chroma_to_tonnetz(hpcp)
return mfcc, hpcp, tonnetz
def compute_features(audio, y_harmonic):
"""Computes the HPCP and MFCC features.
Parameters
----------
audio: np.array(N)
Audio samples of the given input.
y_harmonic: np.array(N)
Harmonic part of the audio signal, in samples.
Returns
-------
mfcc: np.array(N, msaf.Anal.mfcc_coeff)
Mel-frequency Cepstral Coefficients.
hpcp: np.array(N, 12)
Pitch Class Profiles.
tonnetz: np.array(N, 6)
Tonal Centroid features.
cqt: np.array(N, msaf.Anal.cqt_bins)
Constant-Q log-scale features.
gmt: np.array(N, msaf.Anal.mfcc_coeff+6)
Gammatone features
"""
logging.info("Computing Spectrogram...")
S = librosa.feature.melspectrogram(audio,
sr=msaf.Anal.sample_rate,
n_fft=msaf.Anal.frame_size,
hop_length=msaf.Anal.hop_size,
n_mels=msaf.Anal.n_mels)
logging.info("Computing Constant-Q...")
cqt = librosa.logamplitude(librosa.cqt(audio, sr=msaf.Anal.sample_rate,
hop_length=msaf.Anal.hop_size,
n_bins=msaf.Anal.cqt_bins)**2,
ref_power=np.max).T
# cqt = librosa.logamplitude(np.abs(librosa.cqt(audio, sr=msaf.Anal.sample_rate,
# hop_length=msaf.Anal.hop_size,
# n_bins=msaf.Anal.cqt_bins, real=False))**2,
# ref_power=np.max).T
logging.info("Computing MFCCs...")
log_S = librosa.logamplitude(S, ref_power=np.max)
mfcc = librosa.feature.mfcc(S=log_S, n_mfcc=msaf.Anal.mfcc_coeff).T
logging.info("Computing HPCPs...")
# hpcp = librosa.feature.chroma_cqt(y=y_harmonic,
# sr=msaf.Anal.sample_rate,
# hop_length=msaf.Anal.hop_size,
# n_octaves=msaf.Anal.n_octaves,
# fmin=msaf.Anal.f_min).T
hpcp = librosa.feature.chroma_cqt(y=y_harmonic,
sr=msaf.Anal.sample_rate,
hop_length=msaf.Anal.hop_size,
n_octaves=msaf.Anal.n_octaves,
n_chroma=12,
fmin=msaf.Anal.f_min).T
#plt.imshow(hpcp.T, interpolation="nearest", aspect="auto"); plt.show()
logging.info("Computing Tonnetz...")
tonnetz = utils.chroma_to_tonnetz(hpcp)
'''Mi: Extracting Gammatone features'''
logging.info("Computing gammatone features...")
gcc = librosa.feature.gammatone_cepstral_coeffecients(audio, sr=msaf.Anal.sample_rate, nfft=msaf.Anal.frame_size*2,\
hop_length=msaf.Anal.hop_size, nfilters=64, f_min=50,\
f_max=msaf.Anal.sample_rate/2, nCoeff=msaf.Anal.mfcc_coeff, log=False).T
gc = librosa.feature.gammatone_contrast(audio, sr=msaf.Anal.sample_rate, nfft=msaf.Anal.frame_size*2, hop_length=msaf.Anal.hop_size,\
nfilters=64, f_min=50, f_max=msaf.Anal.sample_rate/2, n_bands=6, quantile=0.02, log=False).T
gmt = np.hstack((gcc, gc))
# logging.info("%s" %(hpcp.shape,))
# logging.info("%s" %(gmt.shape,))
return mfcc, hpcp, tonnetz, cqt, gmt
