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Speech Emotion Recognition Using Local and Global Features Read chapter Read first chapter

2017 | OriginalPaper | Chapter

Learning Music Emotions via Quantum Convolutional Neural Network

Authors : Gong Chen, Yan Liu, Jiannong Cao, Shenghua Zhong, Yang Liu, Yuexian Hou, Peng Zhang

Published in: Brain Informatics

Publisher: Springer International Publishing

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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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Metadata
Title
Learning Music Emotions via Quantum Convolutional Neural Network
Authors
Gong Chen
Yan Liu
Jiannong Cao
Shenghua Zhong
Yang Liu
Yuexian Hou
Peng Zhang
Copyright Year
2017
Book
Brain Informatics

Print ISBN: 978-3-319-70771-6

Electronic ISBN: 978-3-319-70772-3

Copyright Year: 2017

DOI
https://doi.org/10.1007/978-3-319-70772-3_5

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