Segmentation of Musical Signals Using Hidden Markov Models.

Sandler M, Aucouturier JJ. Segmentation of Musical Signals Using Hidden Markov Models. In: AES Convention 110. Audio Engineering Society; 2001. Paper 5379. Available from: https://aes.org/publications/elibrary-page/?id=9928

@inproceedings{Sandler2001_9928,
  author = {Sandler, Mark and Aucouturier, Jean-Julien},
  title = {{Segmentation of Musical Signals Using Hidden Markov Models.}},
  booktitle = {AES Convention 110},
  note = {Paper 5379},
  year = {2001},
  month = may,
  publisher = {Audio Engineering Society},
  url = {https://aes.org/publications/elibrary-page/?id=9928}
}

TY  - CPAPER
TI  - Segmentation of Musical Signals Using Hidden Markov Models.
AU  - Sandler, Mark
AU  - Aucouturier, Jean-Julien
T2  - AES Convention 110
M1  - Paper 5379
PY  - 2001
DA  - 2001/05/06
UR  - https://aes.org/publications/elibrary-page/?id=9928
PB  - Audio Engineering Society
LA  - en
AB  - In this paper, we present a segmentation algorithm for acoustic musical signals, using a hidden Markov model. Through unsupervised learning, we discover regions in the music that present steady statistical properties: textures. We investigate different front-ends for the system, and compare their performances. We then show that the obtained segmentation often translates a structure explained by musicology: chorus and verse, different instrumental sections, etc. Finally, we discuss the necessity of the HMM and conclude that an efficient segmentation of music is more than a static clustering and should make use of the dynamics of the data.
ER  -