Blind source separation for robot audition using fixed HRTF beamforming

被引:0
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作者
Mounira Maazaoui
Karim Abed-Meraim
Yves Grenier
机构
[1] Institute Telecom,
[2] Telecom ParisTech,undefined
来源
EURASIP Journal on Advances in Signal Processing | / 2012卷
关键词
Independent Component Analysis; Blind Source Separation; Short Time Fourier Transform; Microphone Array; Natural Gradient;
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摘要
In this article, we present a two-stage blind source separation (BSS) algorithm for robot audition. The first stage consists in a fixed beamforming preprocessing to reduce the reverberation and the environmental noise. Since we are in a robot audition context, the manifold of the sensor array in this case is hard to model due to the presence of the head of the robot, so we use pre-measured head related transfer functions (HRTFs) to estimate the beamforming filters. The use of the HRTF to estimate the beamformers allows to capture the effect of the head on the manifold of the microphone array. The second stage is a BSS algorithm based on a sparsity criterion which is the minimization of the l1 norm of the sources. We present different configuration of our algorithm and we show that it has promising results and that the fixed beamforming preprocessing improves the separation results.
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