A Provably Correct and Robust Algorithm for Convolutive Nonnegative Matrix Factorization
This provides a provably correct method for CNMF, addressing a gap in audio source separation and neural sequence identification, though it is incremental as it builds on existing separable NMF algorithms.
The paper tackles the problem of convolutive nonnegative matrix factorization (CNMF) by proposing a provably correct algorithm under separability assumptions, which guarantees a solution in low noise settings and runs in polynomial time, as demonstrated on synthetic datasets and a singing bird audio sequence.
In this paper, we propose a provably correct algorithm for convolutive nonnegative matrix factorization (CNMF) under separability assumptions. CNMF is a convolutive variant of nonnegative matrix factorization (NMF), which functions as an NMF with additional sequential structure. This model is useful in a number of applications, such as audio source separation and neural sequence identification. While a number of heuristic algorithms have been proposed to solve CNMF, to the best of our knowledge no provably correct algorithms have been developed. We present an algorithm that takes advantage of the NMF model underlying CNMF and exploits existing algorithms for separable NMF to provably find a solution under certain conditions. Our approach guarantees the solution in low noise settings, and runs in polynomial time. We illustrate its effectiveness on synthetic datasets, and on a singing bird audio sequence.