Learning Music Emotions via Quantum Convolutional Neural Network

Gong Chen, Yan Liu*, Jiannong Cao, Shenghua Zhong, Yang LIU, Yuexian Hou, Peng Zhang

*Corresponding author for this work

Research output: Chapter in book/report/conference proceedingConference proceedingpeer-review

5 Citations (Scopus)


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.

Original languageEnglish
Title of host publicationBrain Informatics - International Conference, BI 2017, Proceedings
EditorsYi Zeng, Bo Xu, Maryann Martone, Yong He, Hanchuan Peng, Qingming Luo, Jeanette Hellgren Kotaleski
PublisherSpringer Verlag
Number of pages10
ISBN (Print)9783319707716
Publication statusPublished - 2017
Event2017 International Conference on Brain Informatics, BI 2017 - Beijing, China
Duration: 16 Nov 201718 Nov 2017

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume10654 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference2017 International Conference on Brain Informatics, BI 2017

Scopus Subject Areas

  • Theoretical Computer Science
  • Computer Science(all)

User-Defined Keywords

  • Convolutional neural network
  • Music emotion
  • Quantum mechanics
  • Superposition collapse


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