A novel sparse model for multi-source localization using distributed microphone array
Thi Ngoc Tho Nguyen, Cagdas Tuna, Shengkui Zhao, Douglas L. Jones
Abstract
When distances between microphone pairs are larger than the half-wavelength of signals, source localization methods using cross-correlation such as time-difference-of-arrival (TDOA), steered response power (SRP) are commonly used in practice. We present here a novel model that expresses microphone pairwise cross-correlations as a sum of autocorrelations of source signals shifted by the relative delays of the signals arriving at the microphone pairs, and weighted by the source power and the distances between the sources and the microphone pairs. The model is formulated as a linear inverse problem and is sparse with respect to the source power map. The source power map, which directly shows the locations of all the sound sources, can be reconstructed using ℓ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> -norm minimization algorithms. We demonstrate the effectiveness of our model in a wildlife monitoring application, where the goal is to locate multiple frogs in a dense chorus.
BibTeX
@inproceedings{icassp2017_anovelsparsemode,
title = {A novel sparse model for multi-source localization using distributed microphone array},
author = {Thi Ngoc Tho Nguyen and Cagdas Tuna and Shengkui Zhao and Douglas L. Jones},
booktitle = {ICASSP 2017},
year = {2017}
}