Unsupervised Classification Techniques for Multipitch Estimation

Grenier Y, Rosier J. Unsupervised Classification Techniques for Multipitch Estimation. In: AES Convention 116. Audio Engineering Society; 2004. Paper 6037. Available from: https://aes.org/publications/elibrary-page/?id=12709

@inproceedings{Grenier2004_12709,
  author = {Grenier, Yves and Rosier, Julie},
  title = {{Unsupervised Classification Techniques for Multipitch Estimation}},
  booktitle = {AES Convention 116},
  note = {Paper 6037},
  year = {2004},
  month = may,
  publisher = {Audio Engineering Society},
  url = {https://aes.org/publications/elibrary-page/?id=12709}
}

TY  - CPAPER
TI  - Unsupervised Classification Techniques for Multipitch Estimation
AU  - Grenier, Yves
AU  - Rosier, Julie
T2  - AES Convention 116
M1  - Paper 6037
PY  - 2004
DA  - 2004/05/06
UR  - https://aes.org/publications/elibrary-page/?id=12709
PB  - Audio Engineering Society
LA  - en
AB  - In this paper, we present a fast and efficient technique for multipitch estimation of musical signals. We deal with mixtures where several instruments are present in a monophonic recording. The approach consists in clustering the spectral peaks of the mixture to obtain a spectral representation of each musical note. These spectra are then used to estimate the fundamental frequencies. We compare two techniques for the classification of the spectral peaks: a K-means procedure and a simpler aggregation technique associated to a criterion that represents the closeness to harmonicity for any couple of frequency peaks. This comparison is made on complex mixtures holding various musical instruments and piano chord mixtures. The effectiveness of the two estimation methods is presented using computation of pitch recognition rates and mean source number estimate.
ER  -