for n in np.arange( currentIteration ):
kernel = dictionary[kernels[n]]
returnSignal[offsets[n]:offsets[n] + kernel.shape[0]] += scales[n]*kernel
# Trim the zero padding
returnSignal = returnSignal[biggestKernelSize:-1]
residual = residual[biggestKernelSize:-1]
return returnSignal, residual, scales, kernels, offsets
if __name__ == "__main__":
if len(sys.argv) < 2:
print "Usage: %s audio.wav" % (sys.argv[0])
sys.exit()
audioData, fs = utility.getAudioData( sys.argv[1] )
# Create impulse
impulse = np.zeros( 10000 )
impulse[0] = 1.0
kernelDictionary = ERBFilters.ERBFiltersToKernels( impulse, ERBFilters.makeERBFilters( fs, 200, 100 ) )
reconstructedSignal, residual, scales, kernels, offsets = matchingPursuit( kernelDictionary, audioData, 16, 0, 1000000 )#(2000.0*audioData.shape[0])/fs )
'''plt.subplot(211)
plt.plot( audioData )
plt.subplot(212)
plt.plot( reconstructedSignal )
plt.plot( residual )
plt.show()'''
basename = os.path.splitext( sys.argv[1] )[0]
utility.writeWav( reconstructedSignal, fs, basename + "Reconstructed.wav" )
scalesKernelsAndOffsets = np.zeros( (scales.shape[0], 3) )
scalesKernelsAndOffsets[:, 0] = scales
scalesKernelsAndOffsets[:, 1] = kernels
scalesKernelsAndOffsets[:, 2] = offsets
np.save( basename + 'ReconstructedScalesKernelsAndOffsets.npy', scalesKernelsAndOffsets )
spectrogram = utility.getSpectrogram( audioData, hop=hop, frameSize=frameSize ) seperator = separateHarmonicPercussive.HarmonicPercussiveSeparator( spectrogram ) harmonicSignal = utility.getSignalFromSpectrogram( seperator.harmonicSpectrogram, hop, np.hanning( frameSize ) ) percussiveSignal = utility.getSignalFromSpectrogram( seperator.percussiveSpectrogram, hop, np.hanning( frameSize ) ) impulse = np.zeros( 10000 ) impulse[0] = 1.0 kernelDictionary = ERBFilters.ERBFiltersToKernels( impulse, ERBFilters.makeERBFilters( fs, 200, 100, 5000 ) ) reconstructedHarmonicSignal, residual, scales, kernels, offsets = spikeGramTest.matchingPursuit( kernelDictionary, harmonicSignal, 16, 0, (2000.0*audioData.shape[0])/fs ) melTransformer = melNoiseTransform.melTransformer( fs ) N = np.min( [percussiveSignal.shape[0], residual.shape[0]] ) reconstructedNoiseSignal = melTransformer.getSignalFromMelGram( melTransformer.getMelGram( percussiveSignal[:N] + residual[:N] ) ) reconstructedNoiseSignal /= np.max( np.abs( reconstructedNoiseSignal ) ) reconstructedHarmonicSignal /= np.max( np.abs( reconstructedHarmonicSignal ) ) N = np.min( [reconstructedHarmonicSignal.shape[0], reconstructedNoiseSignal.shape[0]] ) basename =os.path.splitext( sys.argv[1] )[0] utility.writeWav( reconstructedHarmonicSignal[:N] + reconstructedNoiseSignal[:N], fs, basename + "ReconstructedHarmPerc.wav" ) utility.writeWav( harmonicSignal, fs, basename + "Harm.wav" ) utility.writeWav( percussiveSignal, fs, basename + "Perc.wav" ) utility.writeWav( reconstructedHarmonicSignal, fs, basename + "ReconstructedHarm.wav" ) utility.writeWav( reconstructedNoiseSignal, fs, basename + "ReconstructedPerc+HarmResidual.wav" ) utility.writeWav( residual, fs, basename + "HarmResidual.wav" ) scalesKernelsAndOffsets = np.zeros( (scales.shape[0], 3) ) scalesKernelsAndOffsets[:, 0] = scales scalesKernelsAndOffsets[:, 1] = kernels scalesKernelsAndOffsets[:, 2] = offsets np.save( basename + 'ReconstructedHarmPercScalesKernelsAndOffsets.npy', scalesKernelsAndOffsets )