mfcc_lstm.py

import numpy as np
import tensorflow.keras as keras

def build_model(input_shape):
    """Generates RNN-LSTM model
    :param input_shape (tuple): Shape of input set
    :return model: RNN-LSTM model
    """

    # build network topology
    model = keras.Sequential()

    # 2 LSTM layers
    model.add(keras.layers.LSTM(64, input_shape=input_shape, return_sequences=True))
    model.add(keras.layers.LSTM(64))

    # dense layer
    model.add(keras.layers.Dense(64, activation='relu'))
    model.add(keras.layers.Dropout(0.3))

    # output layer
    model.add(keras.layers.Dense(1, activation='sigmoid'))

    optimiser = keras.optimizers.Adam(learning_rate=0.0001)
    model.compile(optimizer=optimiser,
                  loss='binary_crossentropy',
                  metrics=['accuracy'])

    model.summary()

    return model
 
# input_shape = (X_train.shape[1], X_train.shape[2]) # 130, 13
# model = build_model(input_shape)
# history = model.fit(X_train, y_train, validation_data=(X_validation, y_validation), batch_size=32, epochs=30)