Sound source localization using compressive sensing-based feature extraction and spatial sparsity |
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Authors: | Mehdi Banitalebi Dehkordi Hamid Reza Abutalebi Mohammad Reza Taban |
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Affiliation: | Electrical and Computer Engineering Dept., Yazd University, Yazd, Iran |
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Abstract: | In this paper, we propose a source localization algorithm based on a sparse Fast Fourier Transform (FFT)-based feature extraction method and spatial sparsity. We represent the sound source positions as a sparse vector by discretely segmenting the space with a circular grid. The location vector is related to microphone measurements through a linear equation, which can be estimated at each microphone. For this linear dimensionality reduction, we have utilized a Compressive Sensing (CS) and two-level FFT-based feature extraction method which combines two sets of audio signal features and covers both short-time and long-time properties of the signal. The proposed feature extraction method leads to a sparse representation of audio signals. As a result, a significant reduction in the dimensionality of the signals is achieved. In comparison to the state-of-the-art methods, the proposed method improves the accuracy while the complexity is reduced in some cases. |
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