Indoor Sound Source Localization based on Sparse Bayesian Learning and Compressed Data. Bai, Z., Sun, J., Jensen, J. R., & Christensen, M. G. In *2019 27th European Signal Processing Conference (EUSIPCO)*, pages 1-5, Sep., 2019.

Paper doi abstract bibtex

Paper doi abstract bibtex

In this paper, the problems of indoor sound source localization using a wireless acoustic sensor network are addressed and a new sparse Bayesian learning based algorithm is proposed. Using time delays for the direct paths from candidate source locations to microphone nodes, the proposed algorithm estimates the most likely source location. To reduce the amount of data that must be exchanged between microphone nodes, a Gaussian measurement matrix is multiplied on to each channel and the proposed method operates directly on the compressed data. This is achieved by exploiting sparsity in both the frequency and space domains. The performance is analysed in numerical simulations, where the performance as a function of the reverberation times in investigated, and the results show that the proposed algorithm is robust to reverberation.

@InProceedings{8903069, author = {Z. Bai and J. Sun and J. R. Jensen and M. G. Christensen}, booktitle = {2019 27th European Signal Processing Conference (EUSIPCO)}, title = {Indoor Sound Source Localization based on Sparse Bayesian Learning and Compressed Data}, year = {2019}, pages = {1-5}, abstract = {In this paper, the problems of indoor sound source localization using a wireless acoustic sensor network are addressed and a new sparse Bayesian learning based algorithm is proposed. Using time delays for the direct paths from candidate source locations to microphone nodes, the proposed algorithm estimates the most likely source location. To reduce the amount of data that must be exchanged between microphone nodes, a Gaussian measurement matrix is multiplied on to each channel and the proposed method operates directly on the compressed data. This is achieved by exploiting sparsity in both the frequency and space domains. The performance is analysed in numerical simulations, where the performance as a function of the reverberation times in investigated, and the results show that the proposed algorithm is robust to reverberation.}, keywords = {acoustic generators;acoustic signal processing;Bayes methods;learning (artificial intelligence);microphone arrays;microphones;reverberation;telecommunication computing;wireless sensor networks;indoor sound source localization;compressed data;wireless acoustic sensor network;sparse Bayesian learning based algorithm;candidate source locations;microphone nodes;source location;time delays;direct paths;Gaussian measurement matrix;space domains;frequency domains;reverberation times;Microphones;Arrays;Sparse matrices;Delay effects;Position measurement;Frequency estimation;Bayes methods;Sound Source Localization;Sparse Bayesian Learning;Array Signal Processing;Reverberation Environment}, doi = {10.23919/EUSIPCO.2019.8903069}, issn = {2076-1465}, month = {Sep.}, url = {https://www.eurasip.org/proceedings/eusipco/eusipco2019/proceedings/papers/1570529008.pdf}, }

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