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The architecture of the proposed model: the input spectrogram is passed through the CNN part of the network to extract features, which are then passed through the LSTM part so that the long-term dependencies between them are identified and memorized. Finally, the combined features are fed into the output layer that predicts the class to which the input belongs to.
