The nature of statistical learning theory
The nature of statistical learning theory
Least Squares Support Vector Machine Classifiers
Neural Processing Letters
Voice Source Localization for Automatic Camera Pointing System in Videoconferencing
ICASSP '97 Proceedings of the 1997 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP '97) -Volume 1 - Volume 1
Benchmarking Least Squares Support Vector Machine Classifiers
Machine Learning
Acoustic MIMO Signal Processing (Signals and Communication Technology)
Acoustic MIMO Signal Processing (Signals and Communication Technology)
Estimation of source location based on 2-D MUSIC and its application to speech recognition in cars
ICASSP '01 Proceedings of the Acoustics, Speech, and Signal Processing, 2001. on IEEE International Conference - Volume 05
Time delay estimation in room acoustic environments: an overview
EURASIP Journal on Applied Signal Processing
Speaker Diarization For Multiple-Distant-Microphone Meetings Using Several Sources of Information
IEEE Transactions on Computers
Direction of Arrival Estimation Using the Parameterized Spatial Correlation Matrix
IEEE Transactions on Audio, Speech, and Language Processing
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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.