Convolutive Audio Source Separation using Robust ICA and an intelligent evolving permutation ambiguity solution

TL;DR

This paper presents an improved convolutive audio source separation method that enhances robustness and scalability by replacing complex ICA with Robust ICA and introduces two novel permutation ambiguity solutions using Likelihood Ration Jump and MuSIC algorithms.

Contribution

It introduces a robust ICA-based algorithm with two new permutation ambiguity solutions, improving scalability and performance in convolutive audio source separation.

Findings

Robust ICA outperforms complex FastICA in robustness and performance.

Likelihood Ration Jump reduces permutation ambiguity computational cost.

MuSIC-based method shows promising results in permutation resolution.

Abstract

Audio source separation is the task of isolating sound sources that are active simultaneously in a room captured by a set of microphones. Convolutive audio source separation of equal number of sources and microphones has a number of shortcomings including the complexity of frequency-domain ICA, the permutation ambiguity and the problem's scalabity with increasing number of sensors. In this paper, the authors propose a multiple-microphone audio source separation algorithm based on a previous work of Mitianoudis and Davies (2003). Complex FastICA is substituted by Robust ICA increasing robustness and performance. Permutation ambiguity is solved using two methodologies. The first is using the Likelihood Ration Jump solution, which is now modified to decrease computational complexity in the case of multiple microphones. The application of the MuSIC algorithm, as a preprocessing step to the previous solution, forms a second methodology with promising results.