Sparsity Promoted Non-negative Matrix Factorization for Source Separation and Detection

Abstract:

The effectiveness of non-negative matrix factorization (NMF) depends on a suitable choice of the number of bases, which is often difficult to decide in practice. This paper imposes sparseness on the factorization coefficients in order to determine the number of bases automatically during the decomposition process. The benefit of sparse promotion for NMF is demonstrated through application to sound source separation as well as acoustic-based human fall detection under strong interference.

 

Links:

Document

Conference

Google Scholar

IEEE Abstract

 

Reference:

Plain Text:

Y. Wang, Y. Li, K. C. Ho, A. Zare, M. Skubic, “Sparsity promoted non-negative matrix factorization for source separation and detection,” 2014 19th International Conference on Digital Signal Processing (DSP 2014) , pp. 640-645, August 20-23, 2014.

 

BibTex:

@INPROCEEDINGS{WangZare:2014,
author={Y. Wang and Y. Li and K. C. Ho and A. Zare and M. Skubic,},
booktitle={2014 19th International Conference on Digital Signal Processing (DSP 2014)}, title={Sparsity promoted non-negative matrix factorization for source separation and detection},
year={2014},
month={August},
pages={640-645},
}

Categorized as: