In this section
@inproceedings{Hafezi:2017:10.1109/ICASSP.2017.7952209,
author = {Hafezi, S and Moore, AH and Naylor, P},
doi = {10.1109/ICASSP.2017.7952209},
pages = {516--520},
publisher = {Institute of Electrical and Electronics Engineers (IEEE)},
title = {Multiple source localization using estimation consistency in the time-frequency domain},
url = {http://dx.doi.org/10.1109/ICASSP.2017.7952209},
year = {2017}
}
TY - CPAPER
AB - The extraction of multiple Direction-of-Arrival (DoA) information from estimated spatial spectra can be challenging when such spectra are noisy or the sources are adjacent. Smoothing or clustering techniques are typically used to remove the effect of noise or irregular peaks in the spatial spectra. As we will explain and show in this paper, the smoothing-based techniques require prior knowledge of minimum angular separation of the sources and the clustering-based techniques fail on noisy spatial spectrum. A broad class of localization techniques give direction estimates in each Time Frequency (TF) bin. Using this information as input, a novel technique for obtaining robust localization of multiple simultaneous sources is proposed using Estimation Consistency (EC) in the TF domain. The method is evaluated in the context of spherical microphone arrays. This technique does not require prior knowledge of the sources and by removing the noise in the estimated spatial spectrum makes clustering a reliable and robust technique for multiple DoA extraction from estimated spatial spectra. The results indicate that the proposed technique has the strongest robustness to separation with up to 10° median error for 5° to 180° separation for 2 and 3 sources, compared to the baseline and the state-of-the-art techniques.
AU - Hafezi,S
AU - Moore,AH
AU - Naylor,P
DO - 10.1109/ICASSP.2017.7952209
EP - 520
PB - Institute of Electrical and Electronics Engineers (IEEE)
PY - 2017///
SN - 1520-6149
SP - 516
TI - Multiple source localization using estimation consistency in the time-frequency domain
UR - http://dx.doi.org/10.1109/ICASSP.2017.7952209
UR - http://hdl.handle.net/10044/1/45036
ER -
Speech and Audio Processing Lab
CSP Group, EEE Department
Imperial College London
Exhibition Road, London, SW7 2AZ, United Kingdom