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Feature-Extraction.py
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Feature-Extraction.py
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import librosa
import numpy as np
import pandas as pd
from os import listdir
from os.path import isfile, join
'''
function: extract_features
input: path to mp3 files
output: csv file containing features extracted
This function reads the content in a directory and for each mp3 file detected
reads the file and extracts relevant features using librosa library for audio
signal processing
'''
def extract_feature(path):
id = 1 # Song ID
feature_set = pd.DataFrame() # Feature Matrix
# Individual Feature Vectors
songname_vector = pd.Series()
tempo_vector = pd.Series()
total_beats = pd.Series()
average_beats = pd.Series()
chroma_stft_mean = pd.Series()
chroma_stft_std = pd.Series()
chroma_stft_var = pd.Series()
chroma_cq_mean = pd.Series()
chroma_cq_std = pd.Series()
chroma_cq_var = pd.Series()
chroma_cens_mean = pd.Series()
chroma_cens_std = pd.Series()
chroma_cens_var = pd.Series()
mel_mean = pd.Series()
mel_std = pd.Series()
mel_var = pd.Series()
mfcc_mean = pd.Series()
mfcc_std = pd.Series()
mfcc_var = pd.Series()
mfcc_delta_mean = pd.Series()
mfcc_delta_std = pd.Series()
mfcc_delta_var = pd.Series()
rmse_mean = pd.Series()
rmse_std = pd.Series()
rmse_var = pd.Series()
cent_mean = pd.Series()
cent_std = pd.Series()
cent_var = pd.Series()
spec_bw_mean = pd.Series()
spec_bw_std = pd.Series()
spec_bw_var = pd.Series()
contrast_mean = pd.Series()
contrast_std = pd.Series()
contrast_var = pd.Series()
rolloff_mean = pd.Series()
rolloff_std = pd.Series()
rolloff_var = pd.Series()
poly_mean = pd.Series()
poly_std = pd.Series()
poly_var = pd.Series()
tonnetz_mean = pd.Series()
tonnetz_std = pd.Series()
tonnetz_var = pd.Series()
zcr_mean = pd.Series()
zcr_std = pd.Series()
zcr_var = pd.Series()
harm_mean = pd.Series()
harm_std = pd.Series()
harm_var = pd.Series()
perc_mean = pd.Series()
perc_std = pd.Series()
perc_var = pd.Series()
frame_mean = pd.Series()
frame_std = pd.Series()
frame_var = pd.Series()
# Traversing over each file in path
file_data = [f for f in listdir(path) if isfile (join(path, f))]
for line in file_data:
if ( line[-1:] == '\n' ):
line = line[:-1]
# Reading Song
songname = path + line
y, sr = librosa.load(songname, duration=60)
S = np.abs(librosa.stft(y))
# Extracting Features
tempo, beats = librosa.beat.beat_track(y=y, sr=sr)
chroma_stft = librosa.feature.chroma_stft(y=y, sr=sr)
chroma_cq = librosa.feature.chroma_cqt(y=y, sr=sr)
chroma_cens = librosa.feature.chroma_cens(y=y, sr=sr)
melspectrogram = librosa.feature.melspectrogram(y=y, sr=sr)
rmse = librosa.feature.rmse(y=y)
cent = librosa.feature.spectral_centroid(y=y, sr=sr)
spec_bw = librosa.feature.spectral_bandwidth(y=y, sr=sr)
contrast = librosa.feature.spectral_contrast(S=S, sr=sr)
rolloff = librosa.feature.spectral_rolloff(y=y, sr=sr)
poly_features = librosa.feature.poly_features(S=S, sr=sr)
tonnetz = librosa.feature.tonnetz(y=y, sr=sr)
zcr = librosa.feature.zero_crossing_rate(y)
harmonic = librosa.effects.harmonic(y)
percussive = librosa.effects.percussive(y)
mfcc = librosa.feature.mfcc(y=y, sr=sr)
mfcc_delta = librosa.feature.delta(mfcc)
onset_frames = librosa.onset.onset_detect(y=y, sr=sr)
frames_to_time = librosa.frames_to_time(onset_frames[:20], sr=sr)
# Transforming Features
songname_vector.set_value(id, line) # song name
tempo_vector.set_value(id, tempo) # tempo
total_beats.set_value(id, sum(beats)) # beats
average_beats.set_value(id, np.average(beats))
chroma_stft_mean.set_value(id, np.mean(chroma_stft)) # chroma stft
chroma_stft_std.set_value(id, np.std(chroma_stft))
chroma_stft_var.set_value(id, np.var(chroma_stft))
chroma_cq_mean.set_value(id, np.mean(chroma_cq)) # chroma cq
chroma_cq_std.set_value(id, np.std(chroma_cq))
chroma_cq_var.set_value(id, np.var(chroma_cq))
chroma_cens_mean.set_value(id, np.mean(chroma_cens)) # chroma cens
chroma_cens_std.set_value(id, np.std(chroma_cens))
chroma_cens_var.set_value(id, np.var(chroma_cens))
mel_mean.set_value(id, np.mean(melspectrogram)) # melspectrogram
mel_std.set_value(id, np.std(melspectrogram))
mel_var.set_value(id, np.var(melspectrogram))
mfcc_mean.set_value(id, np.mean(mfcc)) # mfcc
mfcc_std.set_value(id, np.std(mfcc))
mfcc_var.set_value(id, np.var(mfcc))
mfcc_delta_mean.set_value(id, np.mean(mfcc_delta)) # mfcc delta
mfcc_delta_std.set_value(id, np.std(mfcc_delta))
mfcc_delta_var.set_value(id, np.var(mfcc_delta))
rmse_mean.set_value(id, np.mean(rmse)) # rmse
rmse_std.set_value(id, np.std(rmse))
rmse_var.set_value(id, np.var(rmse))
cent_mean.set_value(id, np.mean(cent)) # cent
cent_std.set_value(id, np.std(cent))
cent_var.set_value(id, np.var(cent))
spec_bw_mean.set_value(id, np.mean(spec_bw)) # spectral bandwidth
spec_bw_std.set_value(id, np.std(spec_bw))
spec_bw_var.set_value(id, np.var(spec_bw))
contrast_mean.set_value(id, np.mean(contrast)) # contrast
contrast_std.set_value(id, np.std(contrast))
contrast_var.set_value(id, np.var(contrast))
rolloff_mean.set_value(id, np.mean(rolloff)) # rolloff
rolloff_std.set_value(id, np.std(rolloff))
rolloff_var.set_value(id, np.var(rolloff))
poly_mean.set_value(id, np.mean(poly_features)) # poly features
poly_std.set_value(id, np.std(poly_features))
poly_var.set_value(id, np.var(poly_features))
tonnetz_mean.set_value(id, np.mean(tonnetz)) # tonnetz
tonnetz_std.set_value(id, np.std(tonnetz))
tonnetz_var.set_value(id, np.var(tonnetz))
zcr_mean.set_value(id, np.mean(zcr)) # zero crossing rate
zcr_std.set_value(id, np.std(zcr))
zcr_var.set_value(id, np.var(zcr))
harm_mean.set_value(id, np.mean(harmonic)) # harmonic
harm_std.set_value(id, np.std(harmonic))
harm_var.set_value(id, np.var(harmonic))
perc_mean.set_value(id, np.mean(percussive)) # percussive
perc_std.set_value(id, np.std(percussive))
perc_var.set_value(id, np.var(percussive))
frame_mean.set_value(id, np.mean(frames_to_time)) # frames
frame_std.set_value(id, np.std(frames_to_time))
frame_var.set_value(id, np.var(frames_to_time))
print(songname)
id = id+1
# Concatenating Features into one csv and json format
feature_set['song_name'] = songname_vector # song name
feature_set['tempo'] = tempo_vector # tempo
feature_set['total_beats'] = total_beats # beats
feature_set['average_beats'] = average_beats
feature_set['chroma_stft_mean'] = chroma_stft_mean # chroma stft
feature_set['chroma_stft_std'] = chroma_stft_std
feature_set['chroma_stft_var'] = chroma_stft_var
feature_set['chroma_cq_mean'] = chroma_cq_mean # chroma cq
feature_set['chroma_cq_std'] = chroma_cq_std
feature_set['chroma_cq_var'] = chroma_cq_var
feature_set['chroma_cens_mean'] = chroma_cens_mean # chroma cens
feature_set['chroma_cens_std'] = chroma_cens_std
feature_set['chroma_cens_var'] = chroma_cens_var
feature_set['melspectrogram_mean'] = mel_mean # melspectrogram
feature_set['melspectrogram_std'] = mel_std
feature_set['melspectrogram_var'] = mel_var
feature_set['mfcc_mean'] = mfcc_mean # mfcc
feature_set['mfcc_std'] = mfcc_std
feature_set['mfcc_var'] = mfcc_var
feature_set['mfcc_delta_mean'] = mfcc_delta_mean # mfcc delta
feature_set['mfcc_delta_std'] = mfcc_delta_std
feature_set['mfcc_delta_var'] = mfcc_delta_var
feature_set['rmse_mean'] = rmse_mean # rmse
feature_set['rmse_std'] = rmse_std
feature_set['rmse_var'] = rmse_var
feature_set['cent_mean'] = cent_mean # cent
feature_set['cent_std'] = cent_std
feature_set['cent_var'] = cent_var
feature_set['spec_bw_mean'] = spec_bw_mean # spectral bandwidth
feature_set['spec_bw_std'] = spec_bw_std
feature_set['spec_bw_var'] = spec_bw_var
feature_set['contrast_mean'] = contrast_mean # contrast
feature_set['contrast_std'] = contrast_std
feature_set['contrast_var'] = contrast_var
feature_set['rolloff_mean'] = rolloff_mean # rolloff
feature_set['rolloff_std'] = rolloff_std
feature_set['rolloff_var'] = rolloff_var
feature_set['poly_mean'] = poly_mean # poly features
feature_set['poly_std'] = poly_std
feature_set['poly_var'] = poly_var
feature_set['tonnetz_mean'] = tonnetz_mean # tonnetz
feature_set['tonnetz_std'] = tonnetz_std
feature_set['tonnetz_var'] = tonnetz_var
feature_set['zcr_mean'] = zcr_mean # zero crossing rate
feature_set['zcr_std'] = zcr_std
feature_set['zcr_var'] = zcr_var
feature_set['harm_mean'] = harm_mean # harmonic
feature_set['harm_std'] = harm_std
feature_set['harm_var'] = harm_var
feature_set['perc_mean'] = perc_mean # percussive
feature_set['perc_std'] = perc_std
feature_set['perc_var'] = perc_var
feature_set['frame_mean'] = frame_mean # frames
feature_set['frame_std'] = frame_std
feature_set['frame_var'] = frame_var
# Converting Dataframe into CSV Excel and JSON file
feature_set.to_csv('Emotion_features.csv')
feature_set.to_json('Emotion_features.json')
# Extracting Feature Function Call
extract_feature('Dataset/')