def test_scale_degenerate(self):
orig = numpy.array([-3, -3, -3])
x1, range_old = peakutils.scale(orig, (5, 7))
x2, range_new = peakutils.scale(x1, range_old)
assert_array_almost_equal(x1, [6, 6, 6])
assert_array_almost_equal(x2, orig)
def test_scale_degenerate(self):
orig = numpy.array([-3, -3, -3])
x1, range_old = peakutils.scale(orig, (5, 7))
x2, range_new = peakutils.scale(x1, range_old)
assert_array_almost_equal(x1, [6, 6, 6])
assert_array_almost_equal(x2, orig)
def test_scale(self):
orig = numpy.array([-2, -1, 0.5, 1, 3])
x1, range_old = peakutils.scale(orig, (-10, 8))
x2, range_new = peakutils.scale(x1, range_old)
assert_array_almost_equal(orig, x2)
self.assertTupleEqual(range_new, (-10, 8))
def test_scale(self):
orig = numpy.array([-2, -1, 0.5, 1, 3])
x1, range_old = peakutils.scale(orig, (-10, 8))
x2, range_new = peakutils.scale(x1, range_old)
assert_array_almost_equal(orig, x2)
self.assertTupleEqual(range_new, (-10, 8))
def binstoprocess_onset(Pnm0, sg, fL, fH, Fs, NFFT, olap):
"""
Select the bins with energy higher than RMS mean
:param Pnm0: 0th-order SHD coefficient STFT matrix
:param sg: 32xlength audio signals from em32
:param fL: Lower frequency bound
:param fH: Higher frequency bound
:param Fs: Sampling rate (Hz)
:param NFFT: FFT size
:param olap: (NFFT / olap) is the hop_size of the STFT
:return: Time and frequency indices of the bins that are selected for HiGRID DOA estimation
"""
em32 = EigenmikeEM32()
estr = em32.returnAsStruct()
wts = estr['weights']
f1 = int(fL / Fs * NFFT)
f2 = int(fH / Fs * NFFT)
sgo = np.zeros(sg[0].shape) * 1j
for ind in range(32): # Calculate omnidirectional response
sgo += sg[ind] * wts[ind]
ls = mm.audio.spectrogram.LogarithmicFilteredSpectrogram(sgo,
frame_size=NFFT,
hop_size=NFFT /
olap,
fft_size=NFFT,
num_bands=24,
sample_rate=Fs)
os = mm.features.onsets.superflux(ls) # These are the onsets
os = os / max(os) # These are the onsets in the analysed recording
THR = np.sqrt(np.mean(np.array(os)**2))
pk = indexes(os, thres=THR, min_dist=2)
idx = []
idy = []
for ind in pk:
vect = np.abs(np.abs(np.array(Pnm0[ind, f1:f2 + 1])))
vb = baseline(vect, deg=2)
vect, _ = scale(vect - vb)
THRv = 0.1
ids = indexes(vect, thres=THRv, min_dist=1)
for knd in ids:
idx.append(ind)
idy.append(f1 + knd)
return idx, idy
