Optimization of MFCCs for Time Signature Detection Using Genetic Algorithm-Reference-Cited by-同舟云学术

Optimization of MFCCs for Time Signature Detection Using Genetic Algorithm

Published:2023-07-15 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the Companion Conference on Genetic and Evolutionary Computation
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Author:

Abimbola Jeremiah¹^ORCID,Kostrzewa Daniel¹¹^ORCID,Kasprowski Pawel¹^ORCID

Affiliation:

1. Silesian University of Technology, Gliwice, Poland

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3583133.3590728

Reference18 articles.

1. Time Signature Detection: A Survey

2. Stuart Borthwick and Ron Moy . 2020. Popular music genres: An introduction . Routledge . Stuart Borthwick and Ron Moy. 2020. Popular music genres: An introduction. Routledge.

3. Eugene Coyle and Mikel Gainza . 2007 . Time signature detection by using a multi-resolution audio similarity matrix . In Audio Engineering Society Convention 122 . Audio Engineering Society. Eugene Coyle and Mikel Gainza. 2007. Time signature detection by using a multi-resolution audio similarity matrix. In Audio Engineering Society Convention 122. Audio Engineering Society.

4. W Bas De Haas and Anja Volk . 2016 . Meter detection in symbolic music using inner metric analysis . In International Society for Music Information Retrieval Conference. 441 . W Bas De Haas and Anja Volk. 2016. Meter detection in symbolic music using inner metric analysis. In International Society for Music Information Retrieval Conference. 441.

5. Michaël Defferrard , Kirell Benzi , Pierre Vandergheynst , and Xavier Bresson . 2016 . Fma: A dataset for music analysis. arXiv preprint arXiv:1612.01840 (2016). https://arxiv.org/abs/1612.01840. Michaël Defferrard, Kirell Benzi, Pierre Vandergheynst, and Xavier Bresson. 2016. Fma: A dataset for music analysis. arXiv preprint arXiv:1612.01840 (2016). https://arxiv.org/abs/1612.01840.