Rethinking Non-Negative Matrix Factorization with Implicit Neural Representations

arXiv:2404.04439v1 Announce Type: cross
Abstract: 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 …

abstract applications arxiv audio cs.lg cs.sd data eess.as factorization fourier however implicit neural representations matrix negative type