Sound Source DOA Estimation and Localization in Noisy Reverberant Environments Using Least-Squares Support Vector Machines
This paper presents two new algorithms for mapping the time-differences-of-arrival (TDOAs) measured from the microphone pairs to sound source direction-of-arrival (DOA) and location in room environments based on the least-squares support vector machine (LS-SVM). Least squares (LS) has been widely used in the TDOA based algorithms for sound source DOA estimation or localization to map the measured TDOAs into sound source DOA or location. The drawback of LS mapping is that its performance degrades significantly in some scenarios. To combat this problem, an LS-SVM regression based algorithm for the nonlinear mapping is proposed, which outperforms the LS based algorithm in noisy reverberant rooms. Conventional approaches to sound source localization usually assume that the microphones used are ideal and that the locations of the microphones are also known a priori, which may not be well satisfied in practice. Therefore, the microphone arrays need to be calibrated carefully before use. However, it is not an easy task to calibrate microphone arrays perfectly. In this paper, we also proposed an algorithm for sound source localization based on the LS-SVM, which has the advantage that microphone array calibration is not required. The performance of the proposed algorithms is validated by the simulation results in noisy reverberant environments.
sound source doa estimation least-squares support vector machine sound source localization microphone arrays room reverberation uncalibrated microphone arrays