Convolutive Audio Source Separation using Robust ICA and an intelligent evolving permutation ambiguity solution
This work addresses audio source separation for multi-microphone systems, offering incremental improvements in robustness and efficiency.
The authors tackled convolutive audio source separation by enhancing a prior method with Robust ICA for improved robustness and performance, and introduced two modified permutation ambiguity solutions to reduce computational complexity and improve results.
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.