import librosa

import numpy as np

# extract features/data from wav file and return as np.array

def getData(filename):

return raw

# Old extraction with only 8 features

# # extract features/data from wav file and return as np.array

# def getData(filename):

# # why this hop_length?

# hop_length = 256;

#

# # Load the example clip

# y, sr = librosa.load(filename)

#

# # Short-time Fourier transform (STFT)

# S = np.abs(librosa.stft(y))

#

# # Separate harmonics and percussives into two waveforms

# y_harmonic, y_percussive = librosa.effects.hpss(y)

#

# # Beat track on the percussive signal

# tempo, beat_frames = librosa.beat.beat_track(y=y_percussive, sr=sr)

#

# # Compute MFCC features from the raw signal

# mfcc = librosa.feature.mfcc(y=y, sr=sr, hop_length=hop_length, n_mfcc=13)

#

# # And the first-order differences (delta features)

# mfcc_delta = librosa.feature.delta(mfcc)

#

# # Stack and synchronize between beat events

# # This time, we'll use the mean value (default) instead of median

# beat_mfcc_delta = librosa.feature.sync(np.vstack([mfcc, mfcc_delta]), beat_frames)

#

# # Compute chroma features from the harmonic signal

# chromagram = librosa.feature.chroma_cqt(y=y_harmonic, sr=sr)

#

# # Aggregate chroma features between beat events

# # We'll use the median value of each feature between beat frames

# beat_chroma = librosa.feature.sync(chromagram, beat_frames, aggregate=np.median)

#

# # Finally, stack all beat-synchronous features together

# beat_features = np.vstack([beat_chroma, beat_mfcc_delta])

#

# # Average the energy

# avgEnergy = np.mean(librosa.feature.rmse(y=y))

#

# # Estimate tuning

# tuning = librosa.estimate_tuning(y=y, sr=sr)

#

# # zeroCrossings = np.sum(librosa.core.zero_crossings(y=y))

# avgMelSpectro = np.mean(librosa.feature.melspectrogram(y=y, sr=sr))

#

# avgSpectralContrast = np.mean(librosa.feature.spectral_contrast(S=S, sr=sr))

#

# # raw = np.array([[avgSpectralContrast, avgMelSpectro, np.mean(y_harmonic), np.mean(y_percussive), np.mean(mfcc),

# # np.mean(mfcc_delta), np.mean(beat_mfcc_delta), np.mean(chromagram), np.mean(beat_chroma),

# # np.mean(beat_features), avgEnergy, tuning]])

#

# raw = {avgSpectralContrast, avgMelSpectro, np.mean(y_harmonic), np.mean(y_percussive), np.mean(mfcc),

# np.mean(mfcc_delta), np.mean(beat_mfcc_delta), np.mean(chromagram), np.mean(beat_chroma),

# np.mean(beat_features), avgEnergy, tuning}

#

# norm = [float(i) / sum(raw) for i in raw] # normalise numbers between -1 and 1

#

# data = {"avgSpectralContrast": norm[0], "avgMelSpectro": norm[1], "y_harmonic": norm[2], "y_percussive": norm[3],

# "mfcc": norm[4], "mfcc_delta": norm[5], "beat_mfcc_delta": norm[6], "chromagram": norm[7],

# "beat_chroma": norm[8], "beat_features": norm[9], "avgEnergy": norm[10], "tuning": norm[11]}

#

# return data