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librosa_features.py
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librosa_features.py
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'''
================================================
## VOICEBOOK REPOSITORY ##
================================================
repository name: voicebook
repository version: 1.0
repository link: https://github.com/jim-schwoebel/voicebook
author: Jim Schwoebel
author contact: [email protected]
description: a book and repo to get you started programming voice applications in Python - 10 chapters and 200+ scripts.
license category: opensource
license: Apache 2.0 license
organization name: NeuroLex Laboratories, Inc.
location: Seattle, WA
website: https://neurolex.ai
release date: 2018-09-28
This code (voicebook) is hereby released under a Apache 2.0 license license.
For more information, check out the license terms below.
================================================
## LICENSE TERMS ##
================================================
Copyright 2018 NeuroLex Laboratories, Inc.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http:https://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
================================================
## SERVICE STATEMENT ##
================================================
If you are using the code written for a larger project, we are
happy to consult with you and help you with deployment. Our team
has >10 world experts in Kafka distributed architectures, microservices
built on top of Node.js / Python / Docker, and applying machine learning to
model speech and text data.
We have helped a wide variety of enterprises - small businesses,
researchers, enterprises, and/or independent developers.
If you would like to work with us let us know @ [email protected].
================================================
## LIBROSA_FEATURES.PY ##
================================================
Extracts acoustic features using the LibROSA library;
saves them as mean, standard devaition, amx, min, and median
in different classes: onset, rhythm, spectral, and power categories.
Note this is quite a powerful audio feature set that can be used
for a variety of purposes.
'''
import librosa
import numpy as np
# get statistical features in numpy
def stats(matrix):
mean=np.mean(matrix)
std=np.std(matrix)
maxv=np.amax(matrix)
minv=np.amin(matrix)
median=np.median(matrix)
output=np.array([mean,std,maxv,minv,median])
return output
# get labels for later
def stats_labels(label, sample_list):
mean=label+'_mean'
std=label+'_std'
maxv=label+'_maxv'
minv=label+'_minv'
median=label+'_median'
sample_list.append(mean)
sample_list.append(std)
sample_list.append(maxv)
sample_list.append(minv)
sample_list.append(median)
return sample_list
# featurize with librosa following documentation
# https://librosa.github.io/librosa/feature.html
def librosa_featurize(filename, categorize):
# if categorize == True, output feature categories
print('librosa featurizing: %s'%(filename))
# initialize lists
onset_labels=list()
y, sr = librosa.load(filename)
# FEATURE EXTRACTION
######################################################
# extract major features using librosa
mfcc=librosa.feature.mfcc(y)
poly_features=librosa.feature.poly_features(y)
chroma_cens=librosa.feature.chroma_cens(y)
chroma_cqt=librosa.feature.chroma_cqt(y)
chroma_stft=librosa.feature.chroma_stft(y)
tempogram=librosa.feature.tempogram(y)
spectral_centroid=librosa.feature.spectral_centroid(y)[0]
spectral_bandwidth=librosa.feature.spectral_bandwidth(y)[0]
spectral_contrast=librosa.feature.spectral_contrast(y)[0]
spectral_flatness=librosa.feature.spectral_flatness(y)[0]
spectral_rolloff=librosa.feature.spectral_rolloff(y)[0]
onset=librosa.onset.onset_detect(y)
onset=np.append(len(onset),stats(onset))
# append labels
onset_labels.append('onset_length')
onset_labels=stats_labels('onset_detect', onset_labels)
tempo=librosa.beat.tempo(y)[0]
onset_features=np.append(onset,tempo)
# append labels
onset_labels.append('tempo')
onset_strength=librosa.onset.onset_strength(y)
onset_labels=stats_labels('onset_strength', onset_labels)
zero_crossings=librosa.feature.zero_crossing_rate(y)[0]
rmse=librosa.feature.rmse(y)[0]
# FEATURE CLEANING
######################################################
# onset detection features
onset_features=np.append(onset_features,stats(onset_strength))
# rhythm features (384) - take the first 13
rhythm_features=np.concatenate(np.array([stats(tempogram[0]),
stats(tempogram[1]),
stats(tempogram[2]),
stats(tempogram[3]),
stats(tempogram[4]),
stats(tempogram[5]),
stats(tempogram[6]),
stats(tempogram[7]),
stats(tempogram[8]),
stats(tempogram[9]),
stats(tempogram[10]),
stats(tempogram[11]),
stats(tempogram[12])]))
rhythm_labels=list()
for i in range(13):
rhythm_labels=stats_labels('rhythm_'+str(i), rhythm_labels)
# spectral features (first 13 mfccs)
spectral_features=np.concatenate(np.array([stats(mfcc[0]),
stats(mfcc[1]),
stats(mfcc[2]),
stats(mfcc[3]),
stats(mfcc[4]),
stats(mfcc[5]),
stats(mfcc[6]),
stats(mfcc[7]),
stats(mfcc[8]),
stats(mfcc[9]),
stats(mfcc[10]),
stats(mfcc[11]),
stats(mfcc[12]),
stats(poly_features[0]),
stats(poly_features[1]),
stats(spectral_centroid),
stats(spectral_bandwidth),
stats(spectral_contrast),
stats(spectral_flatness),
stats(spectral_rolloff)]))
spectral_labels=list()
for i in range(13):
spectral_labels=stats_labels('mfcc_'+str(i), spectral_labels)
for i in range(2):
spectral_labels=stats_labels('poly_'+str(i), spectral_labels)
spectral_labels=stats_labels('spectral_cenroid', spectral_labels)
spectral_labels=stats_labels('spectral_bandwidth', spectral_labels)
spectral_labels=stats_labels('spectral_contrast', spectral_labels)
spectral_labels=stats_labels('spectral_flatness', spectral_labels)
spectral_labels=stats_labels('spectral_rolloff', spectral_labels)
# power features
power_features=np.concatenate(np.array([stats(zero_crossings),
stats(rmse)]))
power_labels=list()
power_labels=stats_labels('zero_crossings',power_labels)
power_labels=stats_labels('RMSE', power_labels)
# you can also concatenate the features
if categorize == True:
# can output feature categories if true
features={'onset':onset_features,
'rhythm':rhythm_features,
'spectral':spectral_features,
'power':power_features}
labels={'onset':onset_labels,
'rhythm':rhythm_labels,
'spectral':spectral_labels,
'power': power_labels}
else:
# can output numpy array of everything if we don't need categorizations
features = np.concatenate(np.array([onset_features,
rhythm_features,
spectral_features,
power_features]))
labels=onset_labels+rhythm_labels+spectral_labels+power_labels
return features, labels
# features, labels =librosa_featurize('test.wav', True)
# print(len(features['power']))
# print(len(labels['power']))