DNN-based mask estimation for distributed speech enhancement in spatially unconstrained microphone arrays - Archive ouverte HAL Accéder directement au contenu
Pré-Publication, Document De Travail Année : 2020

DNN-based mask estimation for distributed speech enhancement in spatially unconstrained microphone arrays

Résumé

Deep neural network (DNN)-based speech enhancement algorithms in microphone arrays have now proven to be efficient solutions to speech understanding and speech recognition in noisy environments. However, in the context of ad-hoc microphone arrays, many challenges remain and raise the need for distributed processing. In this paper, we propose to extend a previously introduced distributed DNN-based time-frequency mask estimation scheme that can efficiently use spatial information in form of so-called compressed signals which are pre-filtered target estimations. We study the performance of this algorithm under realistic acoustic conditions and investigate practical aspects of its optimal application. We show that the nodes in the microphone array cooperate by taking profit of their spatial coverage in the room. We also propose to use the compressed signals not only to convey the target estimation but also the noise estimation in order to exploit the acoustic diversity recorded throughout the microphone array.
Fichier principal
Vignette du fichier
disco_furnon.pdf (2.94 Mo) Télécharger le fichier
Origine : Fichiers produits par l'(les) auteur(s)

Dates et versions

hal-02985867 , version 1 (02-11-2020)
hal-02985867 , version 2 (19-05-2021)
hal-02985867 , version 3 (20-08-2021)

Identifiants

Citer

Nicolas Furnon, Romain Serizel, Irina Illina, Slim Essid. DNN-based mask estimation for distributed speech enhancement in spatially unconstrained microphone arrays. 2020. ⟨hal-02985867v1⟩
226 Consultations
306 Téléchargements

Altmetric

Partager

Gmail Facebook X LinkedIn More