Ali Golmakani — accepted AI-conference papers

2024

A Weighted-Variance Variational Autoencoder Model for Speech Enhancement

Ali Golmakani, Mostafa Sadeghi, Xavier Alameda-Pineda, Romain Serizel

ICASSP 2024accepted

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 Gaussian distribution is usually assumed for the generative model, where the speech information is encoded in the varianc…

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2023

Audio-Visual Speech Enhancement with a Deep Kalman Filter Generative Model

Ali Golmakani, Mostafa Sadeghi, Romain Serizel

ICASSP 2023accepted

Deep latent variable generative models based on variational autoencoder (VAE) have shown promising performance for audio-visual speech enhancement (AVSE). The underlying idea is to learn a VAE-based audio-visual prior distribution for clean speech data, and then combine it with a statistical noise m…

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