A neural network alternative to non-negative audio models.

A neural network alternative to non-negative audio models. Smaragdis, P. & Venkataramani, S. In 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pages 86–90, March, 2017.

Paper doi abstract bibtex

We present a neural network that can act as an equivalent to a Non-Negative Matrix Factorization (NMF), and further show how it can be used to perform supervised source separation. Due to the extensibility of this approach we show how we can achieve better source separation performance as compared to NMF-based methods, and propose a variety of derivative architectures that can be used for further improvements.

@inproceedings{smaragdis_neural_2017,
	title = {A neural network alternative to non-negative audio models.},
	url = {https://ieeexplore.ieee.org/abstract/document/7952123},
	doi = {10.1109/ICASSP.2017.7952123},
	abstract = {We present a neural network that can act as an equivalent to a Non-Negative Matrix Factorization (NMF), and further show how it can be used to perform supervised source separation. Due to the extensibility of this approach we show how we can achieve better source separation performance as compared to NMF-based methods, and propose a variety of derivative architectures that can be used for further improvements.},
	booktitle = {2017 {IEEE} {International} {Conference} on {Acoustics}, {Speech} and {Signal} {Processing} ({ICASSP})},
	author = {Smaragdis, P. and Venkataramani, S.},
	month = mar,
	year = {2017},
	keywords = {\#nosource},
	pages = {86--90},
}

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