@InProceedings{8902666,
  author = {R. Frisch and M. Faix and J. Droulez and L. Girin and E. Mazer},
  booktitle = {2019 27th European Signal Processing Conference (EUSIPCO)},
  title = {Bayesian time-domain multiple sound source localization for a stochastic machine},
  year = {2019},
  pages = {1-5},
  abstract = {We propose a time-domain multiple sound source localization (SSL) method based on Bayesian inference. This method is specifically designed to run on the stochastic machines (SM) that we are currently developing to perform efficient low-level sensor signal processing with ultra-low power consumption. The proposed SSL method is divided into two main parts. First, a probabilistic model is run on 50 very short time frames (3. 75ms each) of multichannel recorded signals. Second, the results obtained on the different frames are fused to obtain a final localization map. Using the system in a supervised way allows to extract estimated source locations by selecting as many maxima as there are sources in the room. We explain how this method is implemented on a SM. Experiments are presented to illustrate the performance and robustness of the resulting system.},
  keywords = {Bayes methods;inference mechanisms;sensors;signal processing;stochastic processes;stochastic machine;ultra-low power consumption;SSL method;multichannel recorded signals;estimated source locations;Bayesian time-domain multiple sound source localization;Bayesian inference;low-level sensor signal processing;Microphones;Stochastic processes;Position measurement;Bayes methods;Time-domain analysis;Probabilistic logic;Signal processing;Multiple sound source localization;time-domain processing;Bayesian stochastic machine;specific hardware},
  doi = {10.23919/EUSIPCO.2019.8902666},
  issn = {2076-1465},
  month = {Sep.},
  url = {https://www.eurasip.org/proceedings/eusipco/eusipco2019/proceedings/papers/1570529514.pdf},
}

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Mazer},\n booktitle = {2019 27th European Signal Processing Conference (EUSIPCO)},\n title = {Bayesian time-domain multiple sound source localization for a stochastic machine},\n year = {2019},\n pages = {1-5},\n abstract = {We propose a time-domain multiple sound source localization (SSL) method based on Bayesian inference. This method is specifically designed to run on the stochastic machines (SM) that we are currently developing to perform efficient low-level sensor signal processing with ultra-low power consumption. The proposed SSL method is divided into two main parts. First, a probabilistic model is run on 50 very short time frames (3. 75ms each) of multichannel recorded signals. Second, the results obtained on the different frames are fused to obtain a final localization map. Using the system in a supervised way allows to extract estimated source locations by selecting as many maxima as there are sources in the room. We explain how this method is implemented on a SM. 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