Performance of speaker localization using microphone array

• Published in: International journal of speech technology Vol. 19; no. 3; pp. 467 - 483
• Main Authors: Visalakshi, R., Dhanalakshmi, P., Palanivel, S.
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.09.2016; Springer Nature B.V
• Subjects: Algorithms; Arrays; Artificial Intelligence; Cross correlation; Delay; Engineering; Errors; Genetic algorithms; Group dynamics; Least squares method; Linear prediction; Localization; Localization method; Mean square errors; Mean square values; Microphones; Phase transitions; Position (location); Signal,Image and Speech Processing; Social Sciences; Sound sources; Space; Speech; Terminology; Time; Time delay; Time lag; Tracking; Word meaning; Microphone array; Time difference of arrival; Genetic algorithm and group search optimization algorithm; Speaker localization; Gauss–Newton nonlinear least square method
• ISSN: 1381-2416; 1572-8110
• DOI: 10.1007/s10772-016-9341-9

Speaker localization is a technique to locate and track an active speaker from multiple acoustic sources using microphone array. Microphone array is used to improve the speech quality of recorded speech signal in meeting room and other places. In this work, the time delay estimation between source and each microphone is calculated using a localization method called time differences of arrival (TDOA). TDOA localization consists of two steps namely (a) a time delay estimator and (b) a localization estimator. For time delay estimation, the generalized cross-correlation using phase transform, the generalized cross correlation using maximum likelihood, linear prediction (LP) residual and the Hilbert envelope of the LP residual are chosen for estimating the location of a person. A new speaker localization algorithm known as group search optimization (GSO) algorithm is proposed. The performance of this algorithm is analyzed and compared with Gauss–Newton nonlinear least square method and genetic algorithm. Experimental results show that the proposed GSO method outperforms the other methods in terms of mean square error, root mean square error, mean absolute error, mean absolute percentage error, euclidean distance and mean absolute relative error.