8 research outputs found
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
Foreground-Background Ambient Sound Scene Separation
International audienceAmbient 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
Single-channel online enhancement of speech corrupted by reverberation and noise
This paper proposes an online single-channel speech enhancement method designed to improve the quality of speech degraded by reverberation and noise. Based on an autoregressive model for the reverberation power and on a hidden Markov model for clean speech production, a Bayesian filtering formulation of the problem is derived and online joint estimation of the acoustic parameters and mean speech, reverberation, and noise powers is obtained in mel-frequency bands. From these estimates, a real-valued spectral gain is derived and spectral enhancement is applied in the short-time Fourier transform (STFT) domain. The method yields state-of-the-art performance and greatly reduces the effects of reverberation and noise while improving speech quality and preserving speech intelligibility in challenging acoustic environments