A Musical Source Separation System Using a Source-Filter Model and Beta-Divergence Non-Negative Matrix Factorization

Lee S, Park SH, Sung KM. A Musical Source Separation System Using a Source-Filter Model and Beta-Divergence Non-Negative Matrix Factorization. In: AES Conference: 42nd International Conference: Semantic Audio. Audio Engineering Society; 2011. Paper P2-4. Available from: https://aes.org/publications/elibrary-page/?id=15948

@inproceedings{Lee2011_15948,
  author = {Lee, Seokjin and Park, Sang Ha and Sung, Koeng-Mo},
  title = {{A Musical Source Separation System Using a Source-Filter Model and Beta-Divergence Non-Negative Matrix Factorization}},
  booktitle = {AES Conference: 42nd International Conference: Semantic Audio},
  note = {Paper P2-4},
  year = {2011},
  month = jul,
  publisher = {Audio Engineering Society},
  url = {https://aes.org/publications/elibrary-page/?id=15948}
}

TY  - CPAPER
TI  - A Musical Source Separation System Using a Source-Filter Model and Beta-Divergence Non-Negative Matrix Factorization
AU  - Lee, Seokjin
AU  - Park, Sang Ha
AU  - Sung, Koeng-Mo
T2  - AES Conference: 42nd International Conference: Semantic Audio
M1  - Paper P2-4
PY  - 2011
DA  - 2011/07/06
UR  - https://aes.org/publications/elibrary-page/?id=15948
PB  - Audio Engineering Society
LA  - en
AB  - A musical source separation algorithm for mono channel signals is presented in this paper. The algorithm is based on a non-negative matrix factorization (NMF) method which factorizes the magnitude spectrum of the input signal into a sum of components, each of which has a fixed magnitude spectra and a time-varying gain. In order to factorize the input spectrum, the input signal is modeled using a source-filter model. The parameters of the source-filter model are estimated by minimizing the beta-divergence from the input spectrum to the reconstructed model. This source-filter model takes advantage of the reliability of the estimated parameter. Simulation experiments were carried out using mixed signals composed of piano and cello. The performance of the proposed algorithm was compared to the basic NMF algorithm using a linear signal model and to a source-filter model NMF algorithm using Kullback-Leibler divergence instead of beta-divergence. According to the results of these simulations, the proposed algorithm has a better separation quality than that found in the previous algorithms.
ER  -