- Download GTZAN dataset
- Pre-process data, including re-sampling and cliping
- mv process.sh under genres and will auto perform pre-process. So each music which all with 30s length will be trim for 10 clip, each clip is 3s, sampling rate is 22050 Hz
sox -r 22050 $j "$NAME"_clip1.au trim 0 3
sox -r 22050 $j "$NAME"_clip2.au trim 3 3
sox -r 22050 $j "$NAME"_clip3.au trim 6 3
...- Pre-processed data can be downloaded HERE
- Need scipy installed
- pythonize.py needs to be in the same directory of
genres_processed
- Package is downloaded HERE
- Make sure numpy is imported in hpc environment, and make sure ssh -X into hpc
module load python/intel/2.7.6
- Run
python signalScattering.py
- You will see figures generated
- Low-pass filter (order of 6) will be designed by scipy.signal.butter, and filtered by scipy.signal.lfilter.
- Low-pass filter (order of 6) will be designed by scipy.signal.butter, and filtered by scipy.signal.lfilter. Here is an example of on of classical clip being filtered by 50Hz low-pass
if __name__ == '__main__':
'''Get my mel-frequency bank'''
melmat, (melfreq, fftfreq) = generate_melbank(0, 6000)
'''Transfrom mel-frequency to time domain'''
melmat_time = freq2timeDomain(melmat)
'''Read in data'''
# samples = pickle.load( open( "./data/data.in", "rb" ) )
samples_small = pickle.load( open( "./data/data_small.in", "rb" ) )
'''Example of performing lowpass on given signal'''
y = butter_lowpass_filter(samples_small['classical'][0][0], 50, 22050, 6)
'''samples_small_scattered will be the scattered result (plus lowpass filtered) from samples_small'''
samples_small_scattered = scatteringHandler(melmat_time, samples_small)