示例#1
0
def main():
    Ns = 44100*400 #44100*60*3 #3000000

    print("Loading data")

    # load data
    #fname = 'sandbox/test_cpea_20120602_part3.wav' # delay = 45.2 ms = 1993 samples (from Friture cross-correlation measurements)
    fname = 'sandbox/gary_moore_separate_chambre.wav'
    #fname = 'sandbox/gary_moore_loner_chambre.wav'
    #fname = 'sandbox/test_cpea_20120602.wav'
    #data, fs, enc = wavread(fname)
    SAMPLING_RATE, data = wavfile.read(fname)

    print(data.shape, data.dtype)
    data = data.astype(np.float64) # convert from integer to floats for computations

    ht = None

    #data = data[:Ns,:]
    data /= 2**16
    #data = data[::-1,:]

    # original signal
    x = data[:Ns,0]
    
    # measured signal
    m = data[:Ns,1]

    print("Data loaded")

    # We will decimate several times
    # no decimation => 1/fs = 23 µs resolution
    # 1 ms resolution => fs = 1000 Hz is enough => can divide the sampling rate by 44 !
    # if I decimate 2 times (2**2 = 4 => 0.092 ms (3 cm) resolution)!
    # if I decimate 3 times (2**3 = 8 => 0.184 ms (6 cm) resolution)!
    # if I decimate 4 times (2**4 = 16 => 0.368 ms (12 cm) resolution)!
    # if I decimate 5 times (2**5 = 32 => 0.7 ms (24 cm) resolution)!
    # (actually, I could fit a gaussian on the cross-correlation peak to get
    # higher resolution even at low sample rates)
    Ndec = 2
    subsampled_sampling_rate = SAMPLING_RATE/2**(Ndec)
    [bdec, adec] = generated_filters.params['dec']
    zfs0 = subsampler_filtic(Ndec, bdec, adec)
    zfs1 = subsampler_filtic(Ndec, bdec, adec)
    
    delayrange_s = DEFAULT_DELAYRANGE # confidence range

    # separate the channels
    x0 = x
    x1 = m
    #subsample them
    print("subsampling x0")
    x0_dec, zfs0 = subsampler(Ndec, bdec, adec, x0, zfs0)
    print("subsampling x1")
    x1_dec, zfs1 = subsampler(Ndec, bdec, adec, x1, zfs1)

    time = 2*delayrange_s
    length = time*subsampled_sampling_rate
    Ns_dec = Ns/2**(Ndec)
    overlap = 0.5
    Nb = int((1./overlap)*(Ns_dec/length - 1))

    old_Xcorr = np.zeros((length), np.complex128)

    print("time =", time)
    print("length =", length)
    print("Ns_dec = ", Ns_dec)
    print("Nb =", Nb)

    # Hann window to mitigate non-periodicity effects
    window = numpy.hanning(length)

    #l2 = length/2 + 1
    # Hann window to mitigate non-periodicity effects
    #window2 = numpy.hanning(l2)

    #f1 = plt.figure()
    #plt1 = f1.add_subplot(1,1,1)
    #f2 = plt.figure()
    #plt2 = f2.add_subplot(1,1,1)

    for i in range(Nb):
        print(i)
        # retrieve last one-second of data

        d0 = x0_dec[i*length*overlap:i*length*overlap + length]
        d1 = x1_dec[i*length*overlap:i*length*overlap + length]

        std0 = numpy.std(d0)
        std1 = numpy.std(d1)
        if std0>0. and std1>0.:
            Xcorr = generalized_cross_correlation(d0, d1)

            #plt1.plot(Xcorr)
            #plt.figure()
            #plt.plot(Xcorr)
            #plt.draw()

            if old_Xcorr != None and old_Xcorr.shape == Xcorr.shape:
                # smoothing
                alpha = 0.3
                smoothed_Xcorr = alpha*Xcorr + (1. - alpha)*old_Xcorr
            else:
                smoothed_Xcorr = Xcorr
            
            #plt2.plot(Xcorr)
            #plt.draw()

            absXcorr = numpy.abs(smoothed_Xcorr)
            ind = argmax(absXcorr)

            #h2_ = fft(absXcorr**2)
            #N = len(h2_)
            #h2_[N/50:-N/50] = 0.
            #h2 = ifft(h2_)
            #ind = argmax(h2)

            #plt.figure()
            #plt.plot(Xcorr)
            #plt.plot(absXcorr)
            #plt.draw()

            # normalize
            #Xcorr_max_norm = Xcorr_unweighted[ind]/(d0.size*std0*std1)
            Xcorr_extremum = smoothed_Xcorr[ind]
            Xcorr_max_norm = abs(smoothed_Xcorr[ind])/(3*numpy.std(smoothed_Xcorr))
            delay_ms = 1e3*float(ind)/subsampled_sampling_rate
        
            # delays larger than the half of the window most likely are actually negative
            if delay_ms > 1e3*time/2.:
                delay_ms -= 1e3*time

            # store for smoothing
            old_Xcorr = smoothed_Xcorr
        else:
            delay_ms = 0.
            Xcorr_max_norm = 0.
            Xcorr_extremum = 0.

        # debug wrong phase detection
        #if Xcorr_extremum < 0.:
        #    numpy.save("Xcorr.npy", Xcorr)
        #    numpy.save("smoothed_Xcorr.npy", smoothed_Xcorr)

        c = 340. # speed of sound, in meters per second (approximate)
        distance_m = delay_ms*1e-3*c

        # home-made measure of the significance
        slope = 0.12
        p = 3
        x = (Xcorr_max_norm>1.)*(Xcorr_max_norm-1.)
        x = (slope*x)**p
        correlation = int((x/(1. + x))*100)
        
        delay_message = "%.1f ms = %.2f m" %(delay_ms, distance_m)

        correlation_message = "%d%%" %(correlation)

        if Xcorr_extremum >= 0:
            polarity_message = "In-phase"
        else:
            polarity_message = "Reversed phase"

        print(ind, delay_message, correlation_message, polarity_message)

    plt.show()
示例#2
0
y = np.cos(2.*np.pi*f*t)

Ndec = 2
subsampled_sampling_rate = SAMPLING_RATE/2**(Ndec)
[bdec, adec] = generated_filters.PARAMS['dec']
zfs0 = subsampler_filtic(Ndec, bdec, adec)

Nb = 10
l = int(Ns/Nb)

Nb0 = Nb/2
print("Nb0 =", Nb0, "Nb1 =", Nb - Nb0)

print("subsample first parts")
for i in range(Nb0):
    ydec, zfs0 = subsampler(Ndec, bdec, adec, y[i*l:(i+1)*l], zfs0)
    if i == 0:
        y_dec = ydec
    else:
        y_dec = np.append(y_dec, ydec)

print("push an empty array to the subsampler", y[i*l:i*l].shape, y[i*l:i*l].size)
ydec, zfs0 = subsampler(Ndec, bdec, adec, y[i*l:i*l], zfs0)
y_dec = np.append(y_dec, ydec)

print("subsample last parts")
for i in range(Nb0, Nb):
    ydec, zfs0 = subsampler(Ndec, bdec, adec, y[i*l:(i+1)*l], zfs0)
    y_dec = np.append(y_dec, ydec)

print("plot")
示例#3
0
def main():
    Ns = 44100 * 400  #44100*60*3 #3000000

    print("Loading data")

    # load data
    #fname = 'sandbox/test_cpea_20120602_part3.wav' # delay = 45.2 ms = 1993 samples (from Friture cross-correlation measurements)
    fname = 'sandbox/gary_moore_separate_chambre.wav'
    #fname = 'sandbox/gary_moore_loner_chambre.wav'
    #fname = 'sandbox/test_cpea_20120602.wav'
    #data, fs, enc = wavread(fname)
    SAMPLING_RATE, data = wavfile.read(fname)

    print(data.shape, data.dtype)
    data = data.astype(
        np.float64)  # convert from integer to floats for computations

    ht = None

    #data = data[:Ns,:]
    data /= 2**16
    #data = data[::-1,:]

    # original signal
    x = data[:Ns, 0]

    # measured signal
    m = data[:Ns, 1]

    print("Data loaded")

    # We will decimate several times
    # no decimation => 1/fs = 23 µs resolution
    # 1 ms resolution => fs = 1000 Hz is enough => can divide the sampling rate by 44 !
    # if I decimate 2 times (2**2 = 4 => 0.092 ms (3 cm) resolution)!
    # if I decimate 3 times (2**3 = 8 => 0.184 ms (6 cm) resolution)!
    # if I decimate 4 times (2**4 = 16 => 0.368 ms (12 cm) resolution)!
    # if I decimate 5 times (2**5 = 32 => 0.7 ms (24 cm) resolution)!
    # (actually, I could fit a gaussian on the cross-correlation peak to get
    # higher resolution even at low sample rates)
    Ndec = 2
    subsampled_sampling_rate = SAMPLING_RATE / 2**(Ndec)
    [bdec, adec] = generated_filters.params['dec']
    zfs0 = subsampler_filtic(Ndec, bdec, adec)
    zfs1 = subsampler_filtic(Ndec, bdec, adec)

    delayrange_s = DEFAULT_DELAYRANGE  # confidence range

    # separate the channels
    x0 = x
    x1 = m
    #subsample them
    print("subsampling x0")
    x0_dec, zfs0 = subsampler(Ndec, bdec, adec, x0, zfs0)
    print("subsampling x1")
    x1_dec, zfs1 = subsampler(Ndec, bdec, adec, x1, zfs1)

    time = 2 * delayrange_s
    length = time * subsampled_sampling_rate
    Ns_dec = Ns / 2**(Ndec)
    overlap = 0.5
    Nb = int((1. / overlap) * (Ns_dec / length - 1))

    old_Xcorr = np.zeros((length), np.complex128)

    print("time =", time)
    print("length =", length)
    print("Ns_dec = ", Ns_dec)
    print("Nb =", Nb)

    # Hann window to mitigate non-periodicity effects
    window = numpy.hanning(length)

    #l2 = length/2 + 1
    # Hann window to mitigate non-periodicity effects
    #window2 = numpy.hanning(l2)

    #f1 = plt.figure()
    #plt1 = f1.add_subplot(1,1,1)
    #f2 = plt.figure()
    #plt2 = f2.add_subplot(1,1,1)

    for i in range(Nb):
        print(i)
        # retrieve last one-second of data

        d0 = x0_dec[i * length * overlap:i * length * overlap + length]
        d1 = x1_dec[i * length * overlap:i * length * overlap + length]

        std0 = numpy.std(d0)
        std1 = numpy.std(d1)
        if std0 > 0. and std1 > 0.:
            Xcorr = generalized_cross_correlation(d0, d1)

            #plt1.plot(Xcorr)
            #plt.figure()
            #plt.plot(Xcorr)
            #plt.draw()

            if old_Xcorr != None and old_Xcorr.shape == Xcorr.shape:
                # smoothing
                alpha = 0.3
                smoothed_Xcorr = alpha * Xcorr + (1. - alpha) * old_Xcorr
            else:
                smoothed_Xcorr = Xcorr

            #plt2.plot(Xcorr)
            #plt.draw()

            absXcorr = numpy.abs(smoothed_Xcorr)
            ind = argmax(absXcorr)

            #h2_ = fft(absXcorr**2)
            #N = len(h2_)
            #h2_[N/50:-N/50] = 0.
            #h2 = ifft(h2_)
            #ind = argmax(h2)

            #plt.figure()
            #plt.plot(Xcorr)
            #plt.plot(absXcorr)
            #plt.draw()

            # normalize
            #Xcorr_max_norm = Xcorr_unweighted[ind]/(d0.size*std0*std1)
            Xcorr_extremum = smoothed_Xcorr[ind]
            Xcorr_max_norm = abs(
                smoothed_Xcorr[ind]) / (3 * numpy.std(smoothed_Xcorr))
            delay_ms = 1e3 * float(ind) / subsampled_sampling_rate

            # delays larger than the half of the window most likely are actually negative
            if delay_ms > 1e3 * time / 2.:
                delay_ms -= 1e3 * time

            # store for smoothing
            old_Xcorr = smoothed_Xcorr
        else:
            delay_ms = 0.
            Xcorr_max_norm = 0.
            Xcorr_extremum = 0.

        # debug wrong phase detection
        #if Xcorr_extremum < 0.:
        #    numpy.save("Xcorr.npy", Xcorr)
        #    numpy.save("smoothed_Xcorr.npy", smoothed_Xcorr)

        c = 340.  # speed of sound, in meters per second (approximate)
        distance_m = delay_ms * 1e-3 * c

        # home-made measure of the significance
        slope = 0.12
        p = 3
        x = (Xcorr_max_norm > 1.) * (Xcorr_max_norm - 1.)
        x = (slope * x)**p
        correlation = int((x / (1. + x)) * 100)

        delay_message = "%.1f ms = %.2f m" % (delay_ms, distance_m)

        correlation_message = "%d%%" % (correlation)

        if Xcorr_extremum >= 0:
            polarity_message = "In-phase"
        else:
            polarity_message = "Reversed phase"

        print(ind, delay_message, correlation_message, polarity_message)

    plt.show()