TY - GEN
T1 - A neural network alternative to non-negative audio models
AU - Smaragdis, Paris
AU - Venkataramani, Shrikant
N1 - Publisher Copyright:
© 2017 IEEE.
PY - 2017/6/16
Y1 - 2017/6/16
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85023762721&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85023762721&partnerID=8YFLogxK
U2 - 10.1109/ICASSP.2017.7952123
DO - 10.1109/ICASSP.2017.7952123
M3 - Conference contribution
AN - SCOPUS:85023762721
T3 - ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
SP - 86
EP - 90
BT - 2017 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2017 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2017 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2017
Y2 - 5 March 2017 through 9 March 2017
ER -