Phase recovery in NMF for audio source separation: an insightful benchmark
This work addresses phase recovery issues in audio source separation for applications like signal processing, but it is incremental as it focuses on benchmarking existing methods.
The paper tackled the problem of phase recovery in Nonnegative Matrix Factorization (NMF) for audio source separation, which causes audible artifacts, by benchmarking various NMF-based techniques and finding that the High Resolution NMF (HRNMF) model is particularly promising due to its ability to account for phases and correlations over time.
Nonnegative Matrix Factorization (NMF) is a powerful tool for decomposing mixtures of audio signals in the Time-Frequency (TF) domain. In applications such as source separation, the phase recovery for each extracted component is a major issue since it often leads to audible artifacts. In this paper, we present a methodology for evaluating various NMF-based source separation techniques involving phase reconstruction. For each model considered, a comparison between two approaches (blind separation without prior information and oracle separation with supervised model learning) is performed, in order to inquire about the room for improvement for the estimation methods. Experimental results show that the High Resolution NMF (HRNMF) model is particularly promising, because it is able to take phases and correlations over time into account with a great expressive power.