@inproceedings{0f801084bb814314a74c7b632d1dab14,
title = "GENPIA: A Genre-Conditioned Piano Music Generation System",
abstract = "With the demand for music continuing to grow as people seek variety and personal resonance, many works focus on music generation. In this study, we propose GENPIA, a genre-conditioned piano music generation system. The system encompasses Anime, R&B, Jazz, and Classical music genres. To build our system, we collect and label audio data of various genres for the specific objective of our research. REMI audio representation with genre information extension is applied during data pre-processing to present the audio data with a better data structure. Transformer-XL is implemented as the model to learn knowledge about the extended audio representation and generate the desired output audio. An external dataset, called Ailabs.Tw lK7, is utilized for pre-Training purposes. The results obtained from a listening questionnaire show that GENPIA can generate better piano pieces conditioned on different genres compared to the prior state-of-The-Art work.",
keywords = "GENPIA, Genre-condition, Piano music generation, Transfromer-XL",
author = "Nguyen, {Quoc Viet} and Lai, {Hao Wei} and Nguyen, {Khanh Duy} and Sun, {Min Te} and Hwang, {Wu Yuin} and Kazuya Sakai and Ku, {Wei Shinn}",
note = "Publisher Copyright: {\textcopyright} 2024 IEEE.; 5th IEEE International Symposium on the Internet of Sounds, IS2 2024 ; Conference date: 30-09-2024 Through 02-10-2024",
year = "2024",
doi = "10.1109/IS262782.2024.10704094",
language = "???core.languages.en_GB???",
series = "IEEE 5th International Symposium on the Internet of Sounds, IS2 2024",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "IEEE 5th International Symposium on the Internet of Sounds, IS2 2024",
}