def perceptual(x):
coeffs = np.fft.rfft(x, norm='ortho', axis=-1)
scale = zounds.LinearScale.from_sample_rate(samplerate, coeffs.shape[-1])
arr = zounds.ArrayWithUnits(
coeffs,
[x.dimensions[0], zounds.FrequencyDimension(scale)])
arr *= zounds.AWeighting()
samples = np.fft.irfft(arr, norm='ortho', axis=-1)
return zounds.ArrayWithUnits(samples, x.dimensions)
class Sound(Resampled):
"""
A simple pipeline that computes a perceptually weighted modified discrete
cosine transform, and "persists" feature data in an in-memory store.
"""
windowed = zounds.ArrayWithUnitsFeature(
zounds.SlidingWindow,
needs=Resampled.resampled,
wscheme=zounds.HalfLapped(),
wfunc=zounds.OggVorbisWindowingFunc(),
store=True)
mdct = zounds.ArrayWithUnitsFeature(zounds.MDCT, needs=windowed)
weighted = zounds.ArrayWithUnitsFeature(lambda x: x * zounds.AWeighting(),
needs=mdct)
def spectrogram(x):
x = apply_scale(np.abs(x.real),
scale,
window=zounds.OggVorbisWindowingFunc())
x = zounds.log_modulus(x * 100)
return x * zounds.AWeighting()
from log import module_logger
logger = module_logger(__file__)
N_FREQUENCY_BANDS = 512
SAMPLE_RATE = zounds.SR11025()
frequency_band = zounds.FrequencyBand(20, SAMPLE_RATE.nyquist)
scale = zounds.MelScale(frequency_band, N_FREQUENCY_BANDS)
FILTER_BANK_KERNEL_SIZE = 512
FILTER_BANK = zounds.spectral.morlet_filter_bank(SAMPLE_RATE,
FILTER_BANK_KERNEL_SIZE,
scale,
scaling_factor=np.linspace(
0.1, 1.0, len(scale)),
normalize=True)
FILTER_BANK *= zounds.AWeighting()
FILTER_BANK = np.array(FILTER_BANK)
class SpectrogramListener(SoundListener):
def __init__(self, client, s3_client, page_size=3, logger=None):
super().__init__(client, s3_client, page_size, logger)
def _process_samples(self, samples):
samples = samples.mono
samples = zounds.soundfile.resample(samples, SAMPLE_RATE)
windowing_sample_rate = zounds.SampleRate(
frequency=(FILTER_BANK_KERNEL_SIZE // 2) * SAMPLE_RATE.frequency,
duration=FILTER_BANK_KERNEL_SIZE * SAMPLE_RATE.frequency)
windowed = samples.sliding_window(windowing_sample_rate)
windowed = np.asarray(windowed)
chroma = zounds.ArrayWithUnitsFeature(
zounds.Chroma,
frequency_band=band,
window=window,
needs=BaseModel.fft)
if __name__ == '__main__':
app = zounds.ZoundsApp(
model=Sound,
visualization_feature=Sound.chroma,
audio_feature=Sound.ogg,
globals=globals(),
locals=locals())
port = 9999
with app.start_in_thread(port):
url = 'https://ia802606.us.archive.org/9/items/AOC11B/onclassical_luisi_bach_partita_B-flat-major_bwv-825_6.ogg'
_id = Sound.process(meta=url)
snd = Sound(_id)
chroma_scale = zounds.ChromaScale(band)
chroma = chroma_scale.apply(
np.abs(snd.fft) * zounds.AWeighting(), window)
basis = chroma_scale._basis(snd.fft.dimensions[-1].scale, window)
app.start(port)