13 papers
It Takes Few to TANGO: A Quantized Distributed Model for Binaural Speech Enhancement
Zahra Benslimane, Pierre Chouteau, Martyna Poreba +4
Neural network-based multichannel speech enhancement systems achieve strong enhancement performance, but their computational and memory requirements limit deployment on resource-co…
RT-Tango: Real-Time Distributed Binaural Speech Enhancement for Low-Power Hearing Aid Devices
Z. Benslimane, P. Chouteau, M. Poreba +4
Real-time binaural speech enhancement is constrained by latency, computational cost, and inter-device communication, yet existing efficient solutions predominantly address single-c…
Time-Frequency Weighted Losses for Phoneme Reconstruction in DNN-Based Speech Enhancement
Nasser-Eddine Monir, Paul Magron, Romain Serizel
Conventional training losses for speech enhancement based on the signal-to-distortion ratio (SDR) treat all time-frequency (TF) regions uniformly, overlooking the fine-grained spec…
Audio-visual Contrastive Alignment for Diffusion-based Visual-conditioned Speech Enhancement
Colombe Mboungou, Mostafa Sadeghi, Jean-Eudes Ayilo +1
Audio-visual speech enhancement (AVSE) exploits visual cues such as lip movements to recover speech in noisy environments. Recent work introduced diffusion-based unsupervised AVSE,…
Diffusion-based Frameworks for Unsupervised Speech Enhancement
Jean-Eudes Ayilo, Mostafa Sadeghi, Romain Serizel +1
This paper addresses unsupervised diffusion-based single-channel speech enhancement (SE). Prior work in this direction combines a score-based diffusion model trained on clean speec…
Posterior Transition Modeling for Unsupervised Diffusion-Based Speech Enhancement
Mostafa Sadeghi, Jean-Eudes Ayilo, Romain Serizel +1
We explore unsupervised speech enhancement using diffusion models as expressive generative priors for clean speech. Existing approaches guide the reverse diffusion process using no…
Diffusion-based Unsupervised Audio-visual Speech Enhancement
Jean-Eudes Ayilo, Mostafa Sadeghi, Romain Serizel +1
This paper proposes a new unsupervised audio-visual speech enhancement (AVSE) approach that combines a diffusion-based audio-visual speech generative model with a non-negative matr…
Diffusion-based speech enhancement with a weighted generative-supervised learning loss
Jean-Eudes Ayilo, Mostafa Sadeghi, Romain Serizel
Diffusion-based generative models have recently gained attention in speech enhancement (SE), providing an alternative to conventional supervised methods. These models transform cle…
Unsupervised speech enhancement with diffusion-based generative models
Berné Nortier, Mostafa Sadeghi, Romain Serizel
Recently, conditional score-based diffusion models have gained significant attention in the field of supervised speech enhancement, yielding state-of-the-art performance. However,…
Posterior sampling algorithms for unsupervised speech enhancement with recurrent variational autoencoder
Mostafa Sadeghi, Romain Serizel
In this paper, we address the unsupervised speech enhancement problem based on recurrent variational autoencoder (RVAE). This approach offers promising generalization performance o…
Fast and efficient speech enhancement with variational autoencoders
Mostafa Sadeghi, Romain Serizel
Unsupervised speech enhancement based on variational autoencoders has shown promising performance compared with the commonly used supervised methods. This approach involves the use…
Audio-visual speech enhancement with a deep Kalman filter generative model
Ali Golmakani, Mostafa Sadeghi, Romain Serizel
Deep latent variable generative models based on variational autoencoder (VAE) have shown promising performance for audiovisual speech enhancement (AVSE). The underlying idea is to…
A weighted-variance variational autoencoder model for speech enhancement
Ali Golmakani, Mostafa Sadeghi, Xavier Alameda-Pineda +1
We address speech enhancement based on variational autoencoders, which involves learning a speech prior distribution in the time-frequency (TF) domain. A zero-mean complex-valued G…