학술논문

Bayesian time-domain multiple sound source localization for a stochastic machine
Document Type
Conference
Source
2019 27th European Signal Processing Conference (EUSIPCO) Signal Processing Conference (EUSIPCO), 2019 27th European. :1-5 Sep, 2019
Subject
Communication, Networking and Broadcast Technologies
Computing and Processing
Signal Processing and Analysis
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
Language
ISSN
2076-1465
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.