On audio enhancement via online non-negative matrix factorization

TL;DR

This paper introduces an online non-negative matrix factorization approach for noise reduction in audio signals, enabling more memory-efficient and potentially real-time denoising compared to traditional methods.

Contribution

It extends existing NMF-based noise reduction techniques by developing an online algorithm suitable for real-time audio enhancement.

Findings

More memory-efficient than traditional NMF methods.

Potential for real-time audio denoising applications.

Effective noise reduction demonstrated in experiments.

Abstract

We propose a method for noise reduction, the task of producing a clean audio signal from a recording corrupted by additive noise. Many common approaches to this problem are based upon applying non-negative matrix factorization to spectrogram measurements. These methods use a noiseless recording, which is believed to be similar in structure to the signal of interest, and a pure-noise recording to learn dictionaries for the true signal and the noise. One may then construct an approximation of the true signal by projecting the corrupted recording on to the clean dictionary. In this work, we build upon these methods by proposing the use of \emph{online} non-negative matrix factorization for this problem. This method is more memory efficient than traditional non-negative matrix factorization and also has potential applications to real-time denoising.