In this section
@article{Hafezi:2019:10.1121/1.5140191,
author = {Hafezi, S and Moore, AH and Naylor, PA},
doi = {10.1121/1.5140191},
journal = {Journal of the Acoustical Society of America},
pages = {4592--4603},
title = {Spatial consistency for multiple source direction-of-arrival estimation and source counting.},
url = {http://dx.doi.org/10.1121/1.5140191},
volume = {146},
year = {2019}
}
TY - JOUR
AB - A conventional approach to wideband multi-source (MS) direction-of-arrival (DOA) estimation is to perform single source (SS) DOA estimation in time-frequency (TF) bins for which a SS assumption is valid. The typical SS-validity confidence metrics analyse the validity of the SS assumption over a fixed-size TF region local to the TF bin. The performance of such methods degrades as the number of simultaneously active sources increases due to the associated decrease in the size of the TF regions where the SS assumption is valid. A SS-validity confidence metric is proposed that exploits a dynamic MS assumption over relatively larger TF regions. The proposed metric first clusters the initial DOA estimates (one per TF bin) and then uses the members' spatial consistency as well as its cluster's spread to weight each TF bin. Distance-based and density-based clustering are employed as two alternative approaches for clustering DOAs. A noise-robust density-based clustering is also used in an evolutionary framework to propose a method for source counting and source direction estimation. The evaluation results based on simulations and also with real recordings show that the proposed weighting strategy significantly improves the accuracy of source counting and MS DOA estimation compared to the state-of-the-art.
AU - Hafezi,S
AU - Moore,AH
AU - Naylor,PA
DO - 10.1121/1.5140191
EP - 4603
PY - 2019///
SN - 0001-4966
SP - 4592
TI - Spatial consistency for multiple source direction-of-arrival estimation and source counting.
T2 - Journal of the Acoustical Society of America
UR - http://dx.doi.org/10.1121/1.5140191
UR - https://www.ncbi.nlm.nih.gov/pubmed/31893703
UR - https://asa.scitation.org/doi/10.1121/1.5140191
UR - http://hdl.handle.net/10044/1/76390
VL - 146
ER -
Speech and Audio Processing Lab
CSP Group, EEE Department
Imperial College London
Exhibition Road, London, SW7 2AZ, United Kingdom