def test_tdoa_delay_frac_phat():
N = [14, 200, 70, 40, 28]
M = [3, 78, 12, 10, 12]
# important to start at zero and only use positive
# delays due to fractional_delay_filter_bank function
# subtracting minimum delay
delays = [0.0, 0.3, 3.5, 10.1, 5.7]
zero_delay_filter = pra.fractional_delay(0.0)
frac_filters = pra.fractional_delay_filter_bank(delays)
# test without explicit sampling frequency
for n, m, tau, fil in zip(N, M, delays, frac_filters):
pulse = np.random.randn(m)
signal = np.zeros(n)
signal[:m] = pulse
x1 = fftconvolve(zero_delay_filter, signal)
x2 = fftconvolve(signal, fil)
d1 = pra.tdoa(x1, x2, interp=20, phat=True)
d2 = pra.tdoa(x2, x1, interp=20, phat=True)
assert np.allclose(d1, -tau)
assert np.allclose(d2, tau)
fs = 100
d3 = pra.tdoa(x1, x2, interp=20, phat=True, fs=fs)
d4 = pra.tdoa(x2, x1, interp=20, phat=True, fs=fs)
assert np.allclose(d3, -tau / fs)
assert np.allclose(d4, tau / fs)
def make_filters(n_mics):
# Location of original source
azimuth = 61. / 180. * np.pi # 60 degrees
# algorithms parameters
c = 343.
fs = 16000
# circular microphone array, 6 mics, radius 15 cm
R = pra.circular_2D_array([0, 0], n_mics, 0., 0.15)
# propagation filter bank
propagation_vector = -np.array([np.cos(azimuth), np.sin(azimuth)])
delays = np.dot(R.T, propagation_vector) / c * fs # in fractional samples
filter_bank = pra.fractional_delay_filter_bank(delays)
return filter_bank
azimuth = 61.0 / 180.0 * np.pi # 60 degrees
tol = 0.3 / 180.0 * np.pi # 0.3 degrees tolerance for the test
# algorithms parameters
c = 343.0
fs = 16000
nfft = 256
freq_bins = np.arange(5, 60)
# circular microphone array, 12 mics, radius 15 cm
R = pra.circular_2D_array([0, 0], 12, 0.0, 0.15)
# propagation filter bank
propagation_vector = -np.array([np.cos(azimuth), np.sin(azimuth)])
delays = np.dot(R.T, propagation_vector) / c * fs # in fractional samples
filter_bank = pra.fractional_delay_filter_bank(delays)
# we use a white noise signal for the source
x = np.random.randn((nfft // 2 + 1) * nfft)
# convolve the source signal with the fractional delay filters
# to get the microphone input signals
mic_signals = np.array(
[fftconvolve(x, filter, mode="same") for filter in filter_bank])
X = pra.transform.stft.analysis(mic_signals.T,
nfft,
nfft // 2,
win=np.hanning(nfft))
X = np.swapaxes(X, 2, 0)