We present the weighted minimum variance distortionless response (WMVDR), which is a steered response power (SRP) algorithm, for near-field speaker localization in a reverberant environment. The proposed WMVDR is based on a machine learning approach for computing the incoherent frequency fusion of narrowband power maps. We adopt a radial basis function network (RBFN) classifier for the estimation of the weighting coefficients, and a marginal distribution of narrowband power map as feature for the supervised training operation. Simulations demonstrate the effectiveness of the proposed approach in different conditions

Frequency map selection using a RBFN-based classifier in the MVDR beamformer for speaker localization in reverberant rooms

Salvati, Daniele;DRIOLI, Carlo;FORESTI, Gian Luca
2015

Abstract

We present the weighted minimum variance distortionless response (WMVDR), which is a steered response power (SRP) algorithm, for near-field speaker localization in a reverberant environment. The proposed WMVDR is based on a machine learning approach for computing the incoherent frequency fusion of narrowband power maps. We adopt a radial basis function network (RBFN) classifier for the estimation of the weighting coefficients, and a marginal distribution of narrowband power map as feature for the supervised training operation. Simulations demonstrate the effectiveness of the proposed approach in different conditions
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11390/1103912
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