A Neural Network Alternative to Non-Negative Audio Models
This work addresses audio source separation for researchers and practitioners, offering an extensible neural approach with incremental improvements over existing methods.
The paper tackled the problem of audio source separation by proposing a neural network alternative to Non-Negative Matrix Factorization (NMF), achieving better performance compared to NMF-based methods.
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.