Philippe Gonzalez — accepted AI-conference papers

2024

Diffusion-Based Speech Enhancement in Matched and Mismatched Conditions Using a Heun-Based Sampler

Philippe Gonzalez, Zheng-Hua Tan, Jan Østergaard, Jesper Jensen, Tommy Sonne Alstrøm, Tobias May

ICASSP 2024accepted

Diffusion models are a new class of generative models that have recently been applied to speech enhancement successfully. Previous works have demonstrated their superior performance in mismatched conditions compared to state-of-the art discriminative models. However, this was investigated with a sin…

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2023

On Batching Variable Size Inputs for Training End-to-End Speech Enhancement Systems

Philippe Gonzalez, Tommy Sonne Alstrøm, Tobias May

ICASSP 2023accepted

The performance of neural network-based speech enhancement systems is primarily influenced by the model architecture, whereas training times and computational resource utilization are primarily affected by training parameters such as the batch size. Since noisy and reverberant speech mixtures can ha…

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