research area Speech and Natural Language ProcessingIEEE Workshop on Applications of Signal Processing to Audio and Acoustics (WASPAA)
content type paperpublished August 2025

Rethinking Non-Negative Matrix Factorization with Implicit Neural Representations

AuthorsKrishna Subramani†, Paris Smaragdis†, Takuya Higuchi, Mehrez Souden

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This paper was accepted at the IEEE Workshop on Applications of Signal Processing to Audio and Acoustics (WASPAA) 2025

Non-negative Matrix Factorization (NMF) is a powerful technique for analyzing regularly-sampled data, i.e., data that can be stored in a matrix. For audio, this has led to numerous applications using time-frequency (TF) representations like the Short-Time Fourier Transform. However extending these applications to irregularly-spaced TF representations, like the Constant-Q transform, wavelets, or sinusoidal analysis models, has not been possible since these representations cannot be directly stored in matrix form. In this paper, we formulate NMF in terms of learnable functions (instead of vectors) and show that NMF can be extended to a wider variety of signal classes that need not be regularly sampled.

  • † University of Illinois at Urbana-Champaign

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