Low Complex Accurate Multi-Source RTF Estimation
Changheng Li, Jorge Martínez, Richard C. Hendriks
Abstract
Many multi-microphone algorithms depend on knowing the relative acoustic transfer functions (RTFs) of the individual sound sources in the acoustic scene. However, accurate joint RTF estimation for multiple sources is a challenging problem. Existing methods to jointly estimate the RTF for multiple sources have either no satisfying performance, or, suffer from a very large computational complexity. In this paper, we propose a method for robust estimation of the individual RTFs in a multi-source acoustic scenario. The presented algorithm is based on linear algebraic concepts and therefore of lower computational complexity compared to a recently presented state-of-the-art algorithm, while having a similar performance. Experimental results are presented to demonstrate the RTF estimation performance as well as the noise reduction performance when combining the estimated RTFs with a beamformer.
BibTeX
@inproceedings{icassp2022_lowcomplexaccura,
title = {Low Complex Accurate Multi-Source RTF Estimation},
author = {Changheng Li and Jorge Martínez and Richard C. Hendriks},
booktitle = {ICASSP 2022},
year = {2022}
}