Foreground-Background Ambient Sound Scene Separation - Archive ouverte HAL Access content directly
Conference Papers Year : 2021

Foreground-Background Ambient Sound Scene Separation


Ambient sound scenes typically comprise multiple short events occurring on top of a somewhat stationary background. We consider the task of separating these events from the background, which we call foreground-background ambient sound scene separation. We propose a deep learning-based separation framework with a suitable feature normaliza-tion scheme and an optional auxiliary network capturing the background statistics, and we investigate its ability to handle the great variety of sound classes encountered in ambient sound scenes, which have often not been seen in training. To do so, we create single-channel foreground-background mixtures using isolated sounds from the DESED and Audioset datasets, and we conduct extensive experiments with mixtures of seen or unseen sound classes at various signal-to-noise ratios. Our experimental findings demonstrate the generalization ability of the proposed approach.
Fichier principal
Vignette du fichier
conference_101719.pdf (707.15 Ko) Télécharger le fichier
Origin Files produced by the author(s)

Dates and versions

hal-02567542 , version 1 (07-05-2020)
hal-02567542 , version 2 (25-07-2020)



Michel Olvera, Emmanuel Vincent, Romain Serizel, Gilles Gasso. Foreground-Background Ambient Sound Scene Separation. EUSIPCO 2020 - 28th European Signal Processing Conference, Jan 2021, Amsterdam / Virtual, Netherlands. ⟨10.23919/Eusipco47968.2020.9287436⟩. ⟨hal-02567542v2⟩
454 View
578 Download



Gmail Mastodon Facebook X LinkedIn More