Abstract
Music Emotion Recognition (MER), a subject of affective computing, aims to identify the emotion of a musical track. With the fast development of deep learning, neural networks, such as Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM), have been applied recently. However, while using convolutional kernels, channels are treated equally, which means treating different aspects (such as tempo and vibrato related features) of a music clip equally. It’s against the rule of human perception. Therefore, Channel-wise Attention Mechanism is introduced into the task of Music Emotion Recognition. The performance could be improved to a certain extent.
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This work was supported in part by National Key R&D Program of China (2019YFC1711800), NSFC (61671156).
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Chen, X., Wang, L., Pan, A., Li, W. (2021). Channel-wise Attention Mechanism in Convolutional Neural Networks for Music Emotion Recognition. In: Shao, X., Qian, K., Zhou, L., Wang, X., Zhao, Z. (eds) Proceedings of the 8th Conference on Sound and Music Technology . CSMT 2020. Lecture Notes in Electrical Engineering, vol 761. Springer, Singapore. https://doi.org/10.1007/978-981-16-1649-5_4
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