def abstract_cartoon(wave,
window_size=64,
resample_size=32768,
window_func=signal.hann,
freq=False,
norm=True):
#whole wave analysis using a set transform window
#TODO: resample the "cartoon" to a certain length and find trend.
#resample wave to a set length
resampled_wave = signal.resample(wave.data, resample_size)
sr = wave.sr
#segmentation:
segments = fileio.segment(resampled_wave, window_size)
#TODO: test this
#try frequency:
if freq:
transformed_segments = [fft_to_freq(fft_extract(seg, window_func), sr, 1)[0] for seg in segments]
else:
transformed_segments = [fft_extract(seg, window_func).max() for seg in segments]
#ft = fftpack.dct(wave.data * window_type(len(wave.data)))
#window this?
transformed_segments = syllable.moving_window(transformed_segments, window_size, window_func)
if norm:
transformed_segments = normalize(transformed_segments)
#1st derivative for trend detection:
#return np.diff(transformed_segments)
return transformed_segments
def segmentation(wave, seglen=256, topn=1):
sr = wave.sr
fft_result = [feature.fft_extract(x) for x in fileio.segment(wave.data, seglen)]
freq_at_each_step = [feature.fft_to_freq(fft_res, sr, topn) for fft_res in fft_result]
amp_at_each_step = [x.real.max() for x in fft_result]
return amp_at_each_step, np.hstack(freq_at_each_step)
