def get_regions(rate,sample,num_max_regions): output = list() sample = sample/(2.0**15) intpersec = 100 interval = rate/intpersec subsample = None samplebuf = SampleBuffer(rate) for i in xrange(len(sample)/interval): # get new subsample, do not apply any windows subsample = get_subsample(sample, i*interval, (i+1)*interval, window=False) buffer_waveform = samplebuf.get_arr() max_cor = None if max(subsample) < 0.05: subsample = None else: current_waveform = subsample if samplebuf.get_size() <= len(subsample): current_waveform = subsample[len(subsample)/2:] if buffer_waveform is not None: # truncate the buffer to avoid calculating with the entire stored waveform trunc_buffer_waveform = midsection(buffer_waveform, len(current_waveform)*4) # get the correlation between current subsample and previous subsample data norm_correlation = copy(real(correlation(trunc_buffer_waveform, current_waveform))) # normalize by dividing by geometric mean of waveform energies # www-prima.imag.fr/jlc/papers/IAS95-martin.pdf norm_factor = sum(b**2 for b in trunc_buffer_waveform) norm_factor = sqrt(norm_factor*sum(c**2 for c in current_waveform)) # chop off data from the end when the subsample is wrapping around the previous data # TODO: verify that this is not wrapping the previous subsample data around current subsample max_cor = max(norm_correlation[:-len(current_waveform)])/norm_factor end_period = subsample is None and buffer_waveform is not None end_period = end_period or max_cor is not None and max_cor < 0.35 if end_period: if len(buffer_waveform) > len(current_waveform) * 4: output.append((i*interval-len(buffer_waveform),buffer_waveform)) if num_max_regions is not None and num_max_regions == len(output): break samplebuf.flush() # save previous subsample if subsample is not None: samplebuf.append(subsample) return output
def main(): (rate, sample) = wav.read(sys.argv[1]) sample = sample/(2.0**15) intpersec = 50 interval = rate/intpersec for i in xrange(len(sample)/interval): subsample = get_subsample(sample, i*interval, (i+1)*interval) transform = fft(subsample, rate) rtransform = abs(transform) # step 2: get autocorrelation # step 3: fix autocorrelation accounting for varying areas of correlation (given zero padding) # step 4: find peaks in autocorrelation # step 5: given peaks, perform FFTs # step 6: find peaks in FFTs # step 7: given FFT peaks, calculate actual peak frequencies show_sig(rtransform, i*interval, (i+1)*interval, rate, subsample)
