Bayesian time-domain multiple sound source localization for a stochastic machine - Archive ouverte HAL Accéder directement au contenu
Communication Dans Un Congrès Année : 2019

Bayesian time-domain multiple sound source localization for a stochastic machine

Résumé

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.
Fichier principal
Vignette du fichier
1570529514.pdf (224.84 Ko) Télécharger le fichier
Origine : Fichiers produits par l'(les) auteur(s)
Loading...

Dates et versions

hal-02377220 , version 1 (23-11-2019)

Identifiants

Citer

Raphael Frisch, Marvin Faix, Jacques Droulez, Laurent Girin, Emmanuel Mazer. Bayesian time-domain multiple sound source localization for a stochastic machine. EUSIPCO 2019 - 27th European Signal Processing Conference, Sep 2019, A Coruna, Spain. pp.1-5, ⟨10.23919/EUSIPCO.2019.8902666⟩. ⟨hal-02377220⟩
84 Consultations
138 Téléchargements

Altmetric

Partager

Gmail Facebook X LinkedIn More