Voice Analytics project to classify the audio files based on tone and other features.

Sounds travel as a wave. The amplitude of the wave is related to the amount of acoustic energy it carries, or how loud the sound will appear to be. As the amplitude of the sound wave increases, the sound is perceived to be louder.

Extraction of features is a very important part in analyzing and finding relations between different things. As we already know that the data provided of audio cannot be understood by the models directly so we need to convert them into an understandable format for which feature extraction is used.

The audio signal is a three-dimensional signal in which three axes represent time, amplitude and frequency

Zero Crossing Rate : The rate of sign-changes of the signal during the duration of a particular frame.

Energy : The sum of squares of the signal values, normalized by the respective frame length.

Entropy of Energy : The entropy of sub-frames’ normalized energies. It can be interpreted as a measure of abrupt changes.

Spectral Centroid : The center of gravity of the spectrum.

Spectral Spread : The second central moment of the spectrum.

Spectral Entropy : Entropy of the normalized spectral energies for a set of sub-frames.

Spectral Flux : The squared difference between the normalized magnitudes of the spectra of the two successive frames.

Spectral Rolloff : The frequency below which 90% of the magnitude distribution of the spectrum is concentrated.

MFCCs Mel Frequency Cepstral Coefficients form a cepstral representation where the frequency bands are not linear but distributed according to the mel-scale.

Chroma Vector : A 12-element representation of the spectral energy where the bins represent the 12 equal-tempered pitch classes of western-type music (semitone spacing).

Chroma Deviation : The standard deviation of the 12 chroma coefficients.

Noise, Stretch, Shift, Pitch

model=Sequential() model.add(Conv1D(256, kernel_size=5, strides=1, padding='same', activation='relu', input_shape=(x_train.shape[1], 1))) model.add(MaxPooling1D(pool_size=5, strides = 2, padding = 'same'))

model.add(Conv1D(256, kernel_size=5, strides=1, padding='same', activation='relu')) model.add(MaxPooling1D(pool_size=5, strides = 2, padding = 'same'))

model.add(Conv1D(128, kernel_size=5, strides=1, padding='same', activation='relu')) model.add(MaxPooling1D(pool_size=5, strides = 2, padding = 'same')) model.add(Dropout(0.2))

model.add(Conv1D(64, kernel_size=5, strides=1, padding='same', activation='relu')) model.add(MaxPooling1D(pool_size=5, strides = 2, padding = 'same'))

model.add(Flatten()) model.add(Dense(units=32, activation='relu')) model.add(Dropout(0.3))

model.add(Dense(units=2, activation='sigmoid')) model.compile(optimizer = 'adam' , loss = 'binary_crossentropy' , metrics = ['accuracy'])

model.summary()