Automatic Music Transcription Leveraging Generalized Cepstral Features and Deep Learning

Yu Te Wu, Berlin Chen, Li Su

研究成果: 書貢獻/報告類型會議貢獻

2 引文 斯高帕斯(Scopus)

摘要

Spectral features are limited in modeling musical signals with multiple concurrent pitches due to the challenge to suppress the interference of the harmonic peaks from one pitch to another. In this paper, we show that using multiple features represented in both the frequency and time domains with deep learning modeling can reduce such interference. These features are derived systematically from conventional pitch detection functions that relate to one another through the discrete Fourier transform and a nonlinear scaling function. Neural networks modeled with these features outperform state-of-the-art methods while using less training data.

原文英語
主出版物標題2018 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2018 - Proceedings
發行者Institute of Electrical and Electronics Engineers Inc.
頁面401-405
頁數5
ISBN(列印)9781538646588
DOIs
出版狀態已發佈 - 2018 九月 10
事件2018 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2018 - Calgary, 加拿大
持續時間: 2018 四月 152018 四月 20

出版系列

名字ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
2018-April
ISSN(列印)1520-6149

會議

會議2018 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2018
國家加拿大
城市Calgary
期間18/4/1518/4/20

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Electrical and Electronic Engineering

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  • 引用此

    Wu, Y. T., Chen, B., & Su, L. (2018). Automatic Music Transcription Leveraging Generalized Cepstral Features and Deep Learning. 於 2018 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2018 - Proceedings (頁 401-405). [8462079] (ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings; 卷 2018-April). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICASSP.2018.8462079