def spectralFlatness(self, decomposition=True, n_fft=None,
hop_length=None): #win_length=None
"""
Get spectral flatness feature.
:parameters:
- decomposition : boolean. Whether to look at a time-series or
an overall root mean square analysis of the piece.
- win_length : float. Window length for the frames in the
time series analysis. [seconds]. Default is 0.05 s.
- hop_length : float. Hop length (i.e. overlap) between the
frames in the time series analysis [samples].
Default is half-overlap.
"""
if n_fft is None:
n_fft = self.n_fft
# if win_length is None:
# win_length = self.n_fft / self.sr
if hop_length is None:
hop_length = int(self.n_fft / 2)
tmp = extractor.spectralFlatness(self.audio, sr=self.sr,
n_fft=n_fft, hop_length=hop_length,
pad=self.pad, decomposition=decomposition) # win_length=win_length,
self.insertFeature(tmp, 'spectralFlatness', hop_length, full=not decomposition)
self.insertExtractionParameters('spectralFlatness',
dict(hop_length=hop_length,
n_fft=n_fft, pad=self.pad,
decomposition=decomposition, librosa=False)) # win_length=win_length
return tmp
def test_againstMIR_test_alt(self):
val = extractor.spectralFlatness(test_alt, sr, decomposition=False)
MIRVAL = 4.0096e-05
assert np.abs(val - MIRVAL) <= 0.15 * MIRVAL
def test_againstMIR_beethoven(self):
val = extractor.spectralFlatness(beet, sr, decomposition=False)
MIRVAL = 0.024353
assert np.abs(val - MIRVAL) <= 0.15 * MIRVAL