@inproceedings{a2eb7128538d4d3ebbabc2b5025f0760,
title = "Extending music based on emotion and tonality via generative adversarial network",
abstract = "We propose a generative model for music extension in this paper. The model is composed of two classifiers, one for music emotion and one for music tonality, and a generative adversarial network (GAN). Therefore, it can generate symbolic music not only based on low level spectral and temporal characteristics, but also on high level emotion and tonality attributes of previously observed music pieces. The generative model works in a universal latent space constructed by the variational autoencoder (VAE) for representing music pieces. We conduct subjective listening tests and derive objective measures for performance evaluation. Experimental results show that the proposed model produces much smoother and more authentic music pieces than the baseline model in terms of all subjective and objective measures.",
keywords = "Generative adversarial network, Music emotion, Music generation, Tonality, Variational autoencoder",
author = "Tseng, {Bo Wei} and Shen, {Yih Liang} and Chi, {Tai Shih}",
note = "Publisher Copyright: {\textcopyright} 2021 IEEE.; 2021 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2021 ; Conference date: 06-06-2021 Through 11-06-2021",
year = "2021",
month = jun,
doi = "10.1109/ICASSP39728.2021.9413365",
language = "English",
series = "ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "86--90",
booktitle = "2021 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2021 - Proceedings",
address = "美國",
}