Abstract
Music can be used as a form of therapy and can reduce symptoms of depression and anxiety. Understanding the relationship between music and physiological reactions could be essential in further developing music therapy. This paper uses machine learning techniques to classify which genre of music is being listen to using physiological responses. Both Long Short Term Memory Networks and Convolutional Neural Networks can be used for making predictions from sequence data. We trained and compared two networks which attempted to classify the genre of music a participant was listening to from their electrodermal activity. An LSTM and a CNN were trained and their accuracy was found to be 69.23% and 72.97% respectively. Pruning of each of the networks was also conducted and it was found that the network structure for both the CNN and the LSTM can be reduced by at least 20% without having a reduction in the accuracy of the model. It was found that the LSTM has only very few important neurons and weights that contribute to the accuracy of the model.
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Brewer, M., Rahman, J.S. (2020). Pruning Long Short Term Memory Networks and Convolutional Neural Networks for Music Emotion Recognition. In: Yang, H., Pasupa, K., Leung, A.CS., Kwok, J.T., Chan, J.H., King, I. (eds) Neural Information Processing. ICONIP 2020. Lecture Notes in Computer Science(), vol 12534. Springer, Cham. https://doi.org/10.1007/978-3-030-63836-8_29
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DOI: https://doi.org/10.1007/978-3-030-63836-8_29
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