Abstract
Sound source localization is one of the major audiovisual functions of intelligent robots. With the development of computer technology, high-quality sound signal acquisition technology has been widely used in many fields such as microphone array sound source localization. Support Vector Machine (SVM) is a kind of machine learning based on statistical learning theory and structural risk minimization principle. Many parameters will affect the performance of SVM. By changing parameters, the ability of anti-noise can be improved. However it comes at the expense of computation and speed. Proximal support vector machine (PSVM) is an improvement on classical SVM. It has smaller amount of calculation and faster speed. In this paper, by extracting the characteristics of generalized cross-correlation function of sound source signal and using PSVM to locate the sound source, the sound source localization accuracy is better in the reverberation and noise environment, and it has good robust performance.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
