A weighted MVDR beamformer based on SVM learning for sound source localization

作者：Salvati Daniele^*; Drioli Carlo; Foresti Gian Luca

来源：Pattern Recognition Letters, 2016, 84: 15-21.

DOI：10.1016/j.patrec.2016.07.003

• Sound source localization
• Microphone array
• Weighted minimum variance distortionless response
• Support vector machine
• Reverberant environment

A weighted minimum variance distortionless response (WMVDR) algorithm for near-field sound localization in a reverberant environment is presented. The steered response power computation of the WMVDR is based on a machine learning component which improves the incoherent frequency fusion of the narrow-band power maps. A support vector machine (SVM) classifier is adopted to select the components of the fusion. The skewness measure of the narrowband power map marginal distribution is showed to be an effective feature for the supervised learning of the power map selection. Experiments with both simulated and real data demonstrate the improvement of the WMVDR beamformer localization accuracy with respect to other state-of-the-art techniques.

• 出版日期2016-12-1