# A Neural Network Alternative to Non-Negative Audio Models

arXiv: https://arxiv.org/abs/1609.03296 · HTML: https://mlsanity.com/paper/1609.03296/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Sep 2016.

## Built on by (most cited first, 5 of 5)

- [Deep Neural Convolutive Matrix Factorization for Articulatory Representation Decomposition](https://mlsanity.com/paper/2204.00465/) (2022)
- [An extrapolated and provably convergent algorithm for nonlinear matrix decomposition with the ReLU function](https://mlsanity.com/paper/2503.23832/) (2025)
- [Rethinking Non-Negative Matrix Factorization with Implicit Neural Representations](https://mlsanity.com/paper/2404.04439/) (2024)
- [Self-Regularity of Non-Negative Output Weights for Overparameterized Two-Layer Neural Networks](https://mlsanity.com/paper/2103.01887/) (2021)
- [On the Relation Between Autoencoders and Non-negative Matrix Factorization, and Their Application for Mutational Signature Extraction](https://mlsanity.com/paper/2405.07879/) (2024)

## Abstract (arXiv)

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.