A Neural Network Alternative to Non-Negative Audio Models
Not on the map. Why?
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.
Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). First posted on arXiv 12 Sep 2016.
Builds on
No reference list yet.
Built on by
Citing papers per year: 2021: 1; 2022: 1; 2023: 0; 2024: 2; 2025: 1.
5 papers.
- 2022Deep Neural Convolutive Matrix Factorization for Articulatory Representation Decompositioncited by 12
- 2025An extrapolated and provably convergent algorithm for nonlinear matrix decomposition with the ReLU functioncited by 4
- 2024Rethinking Non-Negative Matrix Factorization with Implicit Neural Representationscited by 1
- 2021Self-Regularity of Non-Negative Output Weights for Overparameterized Two-Layer Neural Networkscited by 0
- 2024On the Relation Between Autoencoders and Non-negative Matrix Factorization, and Their Application for Mutational Signature Extractioncited by 0
- 2025An extrapolated and provably convergent algorithm for nonlinear matrix decomposition with the ReLU function31 Mar 2025
- 2024On the Relation Between Autoencoders and Non-negative Matrix Factorization, and Their Application for Mutational Signature Extraction13 May 2024
- 2024Rethinking Non-Negative Matrix Factorization with Implicit Neural Representations5 Apr 2024
- 2022Deep Neural Convolutive Matrix Factorization for Articulatory Representation Decomposition1 Apr 2022
- 2021Self-Regularity of Non-Negative Output Weights for Overparameterized Two-Layer Neural Networks2 Mar 2021