5 papers · 1 filter
End-to-End Multi-Task Learning for Adjustable Joint Noise Reduction and Hearing Loss Compensation
Philippe Gonzalez, Vera Margrethe Frederiksen, Torsten Dau +1
A multi-task learning framework is proposed for optimizing a single deep neural network (DNN) for joint noise reduction (NR) and hearing loss compensation (HLC). A distinct trainin…
Controllable joint noise reduction and hearing loss compensation using a differentiable auditory model
Philippe Gonzalez, Torsten Dau, Tobias May
Deep learning-based hearing loss compensation (HLC) seeks to enhance speech intelligibility and quality for hearing impaired listeners using neural networks. One major challenge of…
The Effect of Training Dataset Size on Discriminative and Diffusion-Based Speech Enhancement Systems
Philippe Gonzalez, Zheng-Hua Tan, Jan Østergaard +3
The performance of deep neural network-based speech enhancement systems typically increases with the training dataset size. However, studies that investigated the effect of trainin…
Diffusion-Based Speech Enhancement in Matched and Mismatched Conditions Using a Heun-Based Sampler
Philippe Gonzalez, Zheng-Hua Tan, Jan Østergaard +3
Diffusion models are a new class of generative models that have recently been applied to speech enhancement successfully. Previous works have demonstrated their superior performanc…
Investigating the Design Space of Diffusion Models for Speech Enhancement
Philippe Gonzalez, Zheng-Hua Tan, Jan Østergaard +3
Diffusion models are a new class of generative models that have shown outstanding performance in image generation literature. As a consequence, studies have attempted to apply diff…