Low Complex Accurate Multi-Source RTF Estimation
Changheng Li (TU Delft - Electrical Engineering, Mathematics and Computer Science)
Jorge Martinez (TU Delft - Electrical Engineering, Mathematics and Computer Science)
Richard C. Hendriks (TU Delft - Electrical Engineering, Mathematics and Computer Science)
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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.