Coherence-adjusted monopole dictionary and convex clustering for 3D localization of mixed near-field and far-field sources

Tomoya Tachikawa; Kohei Yatabe; Yasuhiro Oikawa

ICASSP 2017accepted0 citations

Coherence-adjusted monopole dictionary and convex clustering for 3D localization of mixed near-field and far-field sources

Tomoya Tachikawa, Kohei Yatabe, Yasuhiro Oikawa

Abstract

In this paper, 3D sound source localization method for simultaneously estimating both direction-of-arrival (DOA) and distance from the microphone array is proposed. For estimating distance, the off-grid problem must be overcome because the range of distance to be considered is quite broad and even not bounded. The proposed method estimates positions based on an extension of the convex clustering method combined with sparse coefficients estimation. A method for constructing a suitable monopole dictionary based on coherence is also proposed so that the convex clustering based method appropriately estimate distance of sound sources. Numerical experiments of distance estimation and 3D localization show possibility of the proposed method.

BibTeX

@inproceedings{icassp2017_coherenceadjuste,
  title = {Coherence-adjusted monopole dictionary and convex clustering for 3D localization of mixed near-field and far-field sources},
  author = {Tomoya Tachikawa and Kohei Yatabe and Yasuhiro Oikawa},
  booktitle = {ICASSP 2017},
  year = {2017}
}