TY - GEN
T1 - A novel sparse model for multi-source localization using distributed microphone array
AU - Nguyen, Thi Ngoc Tho
AU - Tuna, Cagdas
AU - Zhao, Shengkui
AU - Jones, Douglas L.
N1 - This study is supported by the research grant for the Human Centered Cyber-physical Systems Programme at the Advanced Digital Sciences Center from Singapore's Agency for Science, Technology and Research (ASTAR).
PY - 2017/6/16
Y1 - 2017/6/16
N2 - 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 ℓ1-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.
AB - 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 ℓ1-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.
KW - cross-correlation
KW - distributed microphone array
KW - linear inverse problem
KW - multi-source
KW - source localization
KW - sparse representation
UR - https://www.scopus.com/pages/publications/85023743888
UR - https://www.scopus.com/pages/publications/85023743888#tab=citedBy
U2 - 10.1109/ICASSP.2017.7952758
DO - 10.1109/ICASSP.2017.7952758
M3 - Conference contribution
AN - SCOPUS:85023743888
T3 - ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
SP - 3256
EP - 3260
BT - 2017 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2017 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2017 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2017
Y2 - 5 March 2017 through 9 March 2017
ER -