On audio enhancement via online non-negative matrix factorization
This work addresses noise reduction in audio processing, but it is incremental as it builds upon existing non-negative matrix factorization methods.
The authors tackled audio noise reduction by proposing online non-negative matrix factorization, which improved memory efficiency and enabled potential real-time denoising applications.
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.