Abstract
Music can convey and evoke powerful emotions. But it is very challenging to recognize the music emotions accurately by computational models. The difficulty of the problem can exponentially increase when the music segments delivery multiple and complex emotions. This paper proposes a novel quantum convolutional neural network (QCNN) to learn music emotions. Inheriting the distinguished abstraction ability from deep learning, QCNN automatically extracts the music features that benefit emotion classification. The main contribution of this paper is that we utilize measurement postulate to simulate the human emotion awareness in music appreciation. Statistical experiments on the standard dataset shows that QCNN outperforms the classical algorithms as well as the state-of-the-art in the task of music emotion classification. Moreover, we provide demonstration experiment to explain the good performance of the proposed technique from the perspective of physics and psychology.
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Acknowledgments
This work was supported by the National Natural Science Foundation of China under Grants 61373122 and Open Funding Project of Tianjin Key Laboratory of Cognitive Computing and Application. We thank Dr. Haidong Yuan for the helpful discussion on quantum mechanics.
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Chen, G. et al. (2017). Learning Music Emotions via Quantum Convolutional Neural Network. In: Zeng, Y., et al. Brain Informatics. BI 2017. Lecture Notes in Computer Science(), vol 10654. Springer, Cham. https://doi.org/10.1007/978-3-319-70772-3_5
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DOI: https://doi.org/10.1007/978-3-319-70772-3_5
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