Speech/Music classification of audio files using machine learning techniques.
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from os import listdir
from os.path import isfile, join
import numpy as np
import json
class bcolors:
BLUE = '\033[94m'
GREEN = '\033[92m'
YELLOW = '\033[93m'
RED = '\033[91m'
ENDC = '\033[0m'
def arrayFromJSON(JSONPath):
with open(JSONPath) as jsonFile:
rawJSON = json.load(jsonFile)
keys = np.array([])
values = np.array([])
for featureKey, featureValues in rawJSON.items():
if keys.size == 0 or values.size == 0:
keys = np.array(featureKey)
values = np.array(featureValues)
else:
keys = np.append(keys, (np.array(featureKey)))
values = np.vstack((values, np.array(featureValues)))
values = np.transpose(values)
return keys, values
def createSingleFeaturesArray(musicJSONsPath, speechJSONsPath):
print(bcolors.YELLOW + 'Creating single features array' + bcolors.ENDC)
dataset = np.array([])
featureKeys = np.array([])
# Reads the extracted features for the music class
featuresFiles = [file for file in listdir(musicJSONsPath) if isfile(join(musicJSONsPath, file))]
for file in featuresFiles:
if dataset.size == 0:
# Gets feature arrays
featureKeys, musicFeatures = arrayFromJSON(musicJSONsPath + file)
# Initializes dataset array
dataset = np.copy(musicFeatures)
else:
# Gets feature arrays
musicFeatures = arrayFromJSON(musicJSONsPath + file)[1]
dataset = np.vstack((dataset, musicFeatures))
# Initializes target array (0 for music)
target = np.zeros((dataset.shape[0]), dtype=int)
# Reads the extracted features for the speech class
featuresFiles = [file for file in listdir(speechJSONsPath) if isfile(join(speechJSONsPath, file))]
for file in featuresFiles:
# Gets feature arrays
speechFeatures = arrayFromJSON(speechJSONsPath + file)[1]
dataset = np.vstack((dataset, speechFeatures))
# Appends the new class to the target array (1 for speech)
target = np.hstack((target, np.ones((dataset.shape[0] - target.size), dtype=int)))
return dataset, target, featureKeys
# Details about this part can be found in the link bellow:
# https://scikit-learn.org/stable/modules/feature_selection.html
def featureSelection(dataset, target, featureKeys):
# Selects features based on a variance threshold
from sklearn.feature_selection import VarianceThreshold
print(bcolors.YELLOW + 'Running variance threshold feature selection' + bcolors.ENDC)
varianceThreshold = 0.1
selector = VarianceThreshold(threshold = (varianceThreshold * (1 - varianceThreshold)))
varReducedDataset = selector.fit_transform(dataset)
isRetained = selector.get_support()
print(bcolors.GREEN + 'Retaining features:' + bcolors.ENDC)
for index, retain in enumerate(isRetained):
if retain and index < featureKeys.size:
print(featureKeys[index], end='\t', flush=True)
print(bcolors.RED + '\n\nRemoving features:' + bcolors.ENDC)
for index, retain in enumerate(isRetained):
if not retain and index < featureKeys.size:
print(featureKeys[index], end='\t', flush=True)
print('\n')
# Selects features based on univariate statistical tests
from sklearn.datasets import load_digits
from sklearn.feature_selection import SelectPercentile, mutual_info_classif
print(bcolors.YELLOW + 'Running feature selection based on mutual information' + bcolors.ENDC)
percentileSelector = SelectPercentile(mutual_info_classif, percentile=33)
perReducedDataset = percentileSelector.fit_transform(dataset, target)
isRetained = percentileSelector.get_support()
print(bcolors.BLUE + 'Scores of features:' + bcolors.ENDC)
for index, score in enumerate(percentileSelector.scores_):
print(featureKeys[index] + ' => ' + str(score), end='\t\t', flush=True)
if index%2:
print('')
print('')
print(bcolors.GREEN + 'Retaining features:' + bcolors.ENDC)
for index, retain in enumerate(isRetained):
if retain and index < featureKeys.size:
print(featureKeys[index], end='\t', flush=True)
print(bcolors.RED + '\n\nRemoving features:' + bcolors.ENDC)
for index, retain in enumerate(isRetained):
if not retain and index < featureKeys.size:
print(featureKeys[index], end='\t', flush=True)
print('\n')
# TODO: change the return value after the values of the parameters are decided
# and the feature selection is complete
return dataset
# Details about this part can be found in the link bellow:
# https://scikit-learn.org/stable/modules/preprocessing.html#preprocessing
def standardization(dataset):
from sklearn import preprocessing
print(bcolors.YELLOW + 'Running standardization' + bcolors.ENDC)
# Standardization
scaledDataset = preprocessing.scale(dataset)
print(bcolors.YELLOW + 'Running normalization' + bcolors.ENDC)
# Normalization
normalizedDataset = preprocessing.normalize(dataset, norm='l2')
# TODO: change the return value after the values of the parameters are decided
# and the feature selection is complete
return dataset
# Details about this part can be found in the link bellow:
# https://scikit-learn.org/stable/modules/generated/sklearn.decomposition.PCA.html#sklearn.decomposition.PCA
def PCA(dataset):
from sklearn.decomposition import PCA
print(bcolors.YELLOW + 'Running PCA' + bcolors.ENDC)
pca = PCA(n_components=10, svd_solver='full')
transformedDataset = pca.fit(dataset).transform(dataset)
# TODO: change the return value after the values of the parameters are decided
# and the feature selection is complete
return dataset
# Prints a nice message to let the user know the module was imported
print(bcolors.BLUE + 'feature_preprocessing loaded' + bcolors.ENDC)
# Enables executing the module as a standalone script
if __name__ == "__main__":
import sys
dataset, target, featureKeys = createSingleFeaturesArray(sys.argv[1], sys.argv[2])
PCA(standardization(featureSelection(dataset, target, featureKeys)))
print(bcolors.GREEN + 'Saving results to files' + bcolors.ENDC)
np.save('dataset.npy', dataset)
np.save('target.npy', target)
np.save('featureKeys.npy', featureKeys)