def extract_features(wavfilename, fctr=400, fsd=1.0, type=1):
"""Computes beat-synchronous chroma features from the given wave file
Calls Dan Ellis' chrombeatftrs Matlab function.
"""
x, fs = mlab.wavread(wavfilename, nout=2)
feats, beats = mlab.chrombeatftrs(x, fs, fctr, fsd, type, nout=2)
return feats, beats.flatten()
def extract_features(wavfilename, fctr=400, fsd=1.0, type=1):
"""Computes beat-synchronous chroma features from the given wave file
Calls Dan Ellis' chrombeatftrs Matlab function.
"""
x, fs = mlab.wavread(wavfilename, nout=2)
feats, beats = mlab.chrombeatftrs(x, fs, fctr, fsd, type, nout=2)
return feats, beats.flatten()
def extract_features(wavfilename, fctr=400, fsd=1.0, type=1):
"""Computes beat-synchronous chroma features from the given wave file
Calls Dan Ellis' chrombeatftrs Matlab function.
"""
logger.info('Extracting beat-synchronous chroma features from %s',
wavfilename)
x,fs = mlab.wavread(wavfilename, nout=2)
feats,beats = mlab.chrombeatftrs(x.mean(1)[:,np.newaxis], fs, fctr, fsd,
type, nout=2)
songlen = x.shape[0] / fs
return feats, beats.flatten(), songlen
def extract_features(wavfilename, fctr=400, fsd=1.0, type=1):
"""Computes beat-synchronous chroma features from the given wave file
Calls Dan Ellis' chrombeatftrs Matlab function.
"""
logger.info('Extracting beat-synchronous chroma features from %s',
wavfilename)
x, fs = mlab.wavread(wavfilename, nout=2)
feats, beats = mlab.chrombeatftrs(x.mean(1)[:, np.newaxis],
fs,
fctr,
fsd,
type,
nout=2)
songlen = x.shape[0] / fs
return feats, beats.flatten(), songlen
def extract_features(wavfilename, fctr=400, fsd=1.0, type=1):
"""Computes beat-synchronous chroma features from the given wave file
Calls Dan Ellis' chrombeatftrs Matlab function.
"""
if lower(wavfilename[-4:]) == '.csv':
logger.info('CSV filename reading preprocessed features from %s',
wavfilename)
csvr = csv.reader(open(wavfilename, 'rb'), delimiter=',')
feats = np.array([[float(x) for x in row] for row in csvr])
beats = np.arange(len(feats)) * 0.0058 # simple fake frame locations
songlen = len(feats) * 0.0058
else:
logger.info('Extracting beat-synchronous chroma features from %s',
wavfilename)
x,fs = mlab.wavread(wavfilename, nout=2)
feats,beats = mlab.chrombeatftrs(x.mean(1)[:,np.newaxis], fs, fctr, fsd,
type, nout=2)
songlen = x.shape[0] / fs
return feats, beats.flatten(), songlen
