Low Complex Accurate Multi-Source RTF Estimation

Low Complex Accurate Multi-Source RTF Estimation

Li, L. (TU Delft Signal Processing Systems)
Martinez, Jorge (TU Delft Electrical Engineering Education)
Hendriks, R.C. (TU Delft Signal Processing Systems)

2022

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.

Joint diagonalization
microphone array signal processing
source separation
RTF estimation
speech enhancement

http://resolver.tudelft.nl/uuid:b423776c-458c-4c16-8abe-43ef2dbd3241

https://doi.org/10.1109/ICASSP43922.2022.9747170

IEEE, Piscataway

978-1-6654-0541-6

Proceedings of the ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2022-05-23 → 2022-05-27, Singapore, Singapore

Accepted author manuscript

Institutional Repository

conference paper

© 2022 L. Li, Jorge Martinez, R.C. Hendriks