Dynamic relative impulse response estimation using structured sparse Bayesian learning
Ritwik Giri, Bhaskar D. Rao, Fred Mustiere, Tao Zhang
Abstract
In this paper we present a novel Hierarchical Bayesian approach to estimate Relative Impulse Response (ReIR) using short, noisy and reverberant microphone recordings. The information contained in ReIRs between two microphones is useful for a wide range of multichannel speech processing applications such as speaker localization, speech enhancement, etc. It has been shown in several previous works that the Relative Transfer Function (RTF) corresponding to a given ReIR is dynamic and depends on the environment, microphone positions and target position. This acts as the main motivation of this work, as we develop a structured sparse Bayesian learning algorithm to estimate ReIR using very short recordings, which will be robust to changes in the environment. An extensive experimental study with real-world recordings has also been conducted to show the efficacy of our proposed approach over other competing approaches.
BibTeX
@inproceedings{icassp2016_dynamicrelativei,
title = {Dynamic relative impulse response estimation using structured sparse Bayesian learning},
author = {Ritwik Giri and Bhaskar D. Rao and Fred Mustiere and Tao Zhang},
booktitle = {ICASSP 2016},
year = {2016}
}