Evaluation of parallel and sequential deep learning models for music subgenre classification

Abstract

<p style='text-indent:20px;'>In this paper, we evaluate two deep learning models which integrate convolutional and recurrent neural networks. We implement both sequential and parallel architectures for fine-grain musical subgenre classification. Due to the exceptionally low signal to noise ratio (SNR) of our low level mel-spectrogram dataset, more sensitive yet robust learning models are required to generate meaningful results. We investigate the effects of three commonly applied optimizers, dropout, batch regularization, and sensitivity to varying initialization distributions. The results demonstrate that the sequential model specifically requires the RMSprop optimizer, while the parallel model implemented with the Adam optimizer yielded encouraging and stable results achieving an average F1 score of <inline-formula><tex-math id="M1">\begin{document}$ 0.63 $\end{document}</tex-math></inline-formula>. When all factors are considered, the optimized hybrid parallel model outperformed the sequential in classification accuracy and system stability.