Music Emotion Recognition Using a Variant of Recurrent Neural Network

DOI

10.2991/mmssa-18.2019.4How to use a DOI?

Keywords

music emotion; harmonics and percussive; chroma recurrent neural network

Abstract

Searching music by emotion has always been strongly needed by users. Since music streaming applications usually have tens millions of music pieces in database, it is impossible to label emotion tags for each music piece manually. It is desired that an intelligent algorithm can recognize emotion expressed by music automatically. Valence-Arousal model is a widely used for representing emotion, but the angle of vectors on V-A plane labeled by different raters usually varies greatly, which makes it difficult to train classification models. We are trying to introduce a label space defined by pairs of antonyms, which makes emotion label relatively objective. We also used a variant model of recurrent neural network in the paper, in this model, RNN is a mean to extract features from melody, and with other features calculated by normal machine learning algorithms, this model can make a good prediction of emotions.

Copyright

© 2019, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Huaping Liu
AU  - Yong Fang
AU  - Qinghua Huang
PY  - 2019/01
DA  - 2019/01
TI  - Music Emotion Recognition Using a Variant of Recurrent Neural Network
BT  - Proceedings of the 2018 International Conference on Mathematics, Modeling, Simulation and Statistics Application (MMSSA 2018)
PB  - Atlantis Press
SP  - 15
EP  - 18
SN  - 1951-6851
UR  - https://doi.org/10.2991/mmssa-18.2019.4
DO  - 10.2991/mmssa-18.2019.4
ID  - Liu2019/01
ER  -