def compute_essentia_descriptors(audio_segment, actual_bar_beg,
actual_bar_end):
"""
Computes the values of selected descriptors in the given audio segment.
"""
frames = FrameGenerator(audio_segment,
frameSize=frameSize,
hopSize=hopSize)
mfccs_bar = []
bark_vector = [0] * 27
pool = essentia.Pool()
total_frames = frames.num_frames()
for frame in frames:
frame_windowed = window(frame)
frame_spectrum = spectrum(frame_windowed)
(frame_frequencies, frame_magnitudes) = spectralPeaks(frame_spectrum)
mag, phase, = c2p(fft(frame_windowed))
pool.add('onsets.hfc', od(mag, phase))
frame_dissonance = dissonance(frame_frequencies, frame_magnitudes)
pool.add('dissonance', frame_dissonance)
# pool.add('zerocrossingrate', zerocrossingrate(frame))
mfcc_bands, mfcc_coeffs = mfcc(spectrum(window(frame)))
mfccs_bar.append(mfcc_coeffs)
frame_barkbands = barkbands(frame_spectrum)
for i in range(27):
bark_vector[i] += frame_barkbands[i] / total_frames
onsets_hfc = onsets(essentia.array([pool['onsets.hfc']]), [1])
onset_rate = float(len(onsets_hfc)) / (actual_bar_end - actual_bar_beg)
bar_dissonance = mean(pool["dissonance"])
return mfccs_bar, bark_vector, onset_rate, bar_dissonance
def essFalsestereoDetector(x: list,
frameSize=1024,
hopSize=512,
correlationThreshold=0.98,
percentageThreshold=90,
channels=2,
**kwargs):
"""Computes the correlation and consideres if the information in the two channels is the same
Args:
x: (list) input signal
frameSize: (int) frame size for the analysis in falseStereoDetector
hopSize: (int) hop_size for the analysis in falseStereoDetector
correlationthreshold: (float) lower limit to decide if a file has correlation problems
Returns:
final_bool: (bool) True if the information is the same in both channels, False otherwise
percentace: (float) How many frames were false stereo over all the frames
"""
if channels < 2:
return 1, False, True
rx, lx = StereoDemuxer()(x)
mux = StereoMuxer()
falseStereoDetector = FalseStereoDetector(
correlationThreshold=correlationThreshold, **kwargs)
lfg = FrameGenerator(lx,
frameSize=frameSize,
hopSize=hopSize,
startFromZero=True)
rfg = FrameGenerator(rx,
frameSize=frameSize,
hopSize=hopSize,
startFromZero=True)
problematicFrames = sum([
falseStereoDetector(mux(frameL, frameR))[0]
for frameL, frameR in zip(lfg, rfg)
])
# problematicFrames = []
# for frameL, frameR in zip(lfg, rfg):
# res, corr = falseStereoDetector(mux(frameL, frameR))
# problematicFrames.append(res)
falseStereoDetector.reset()
conf = float(sum(problematicFrames)) / float(lfg.num_frames())
return conf, conf > percentageThreshold / 100, False
def outofPhaseDetector(x: list,
frameSize=1024,
hopSize=512,
correlationThreshold=-0.8,
percentageThreshold=90,
channels=2,
**kwargs):
"""Computes the correlation and flags the file if the file has a 90% of frames out of phase
Args:
x: (list) input signal
frameSize: (int) frame size for the analysis in falseStereoDetector
hopSize: (int) hop_size for the analysis in falseStereoDetector
correlationthreshold: (float) higher limit to decide if a file has correlation problems
Returns:
final_bool: (bool) True if the information is the same in both channels, False otherwise
percentace: (float) How many frames were false stereo over all the frames
"""
if channels < 2:
return 1, False, True
rx, lx = StereoDemuxer()(x)
mux = StereoMuxer()
falseStereoDetector = FalseStereoDetector(**kwargs)
lfg = FrameGenerator(lx,
frameSize=frameSize,
hopSize=hopSize,
startFromZero=True)
rfg = FrameGenerator(rx,
frameSize=frameSize,
hopSize=hopSize,
startFromZero=True)
problematicFrames = 0
for frameL, frameR in zip(lfg, rfg):
_, corr = falseStereoDetector(mux(frameL, frameR))
problematicFrames += corr < correlationThreshold
falseStereoDetector.reset()
conf = problematicFrames / lfg.num_frames()
return conf, conf > percentageThreshold / 100, False