Diffusion-Based Speech Enhancement in Matched and Mismatched Conditions Using a Heun-Based Sampler
This work addresses speech enhancement for audio processing applications, offering incremental improvements in generalization and efficiency.
The authors tackled the problem of speech enhancement in matched and mismatched acoustic conditions by systematically assessing a diffusion-based model using multiple databases and a novel Heun-based sampler, achieving superior performance compared to state-of-the-art discriminative models with reduced computational cost.
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 single database for training and another one for testing, which makes the results highly dependent on the particular databases. Moreover, recent developments from the image generation literature remain largely unexplored for speech enhancement. These include several design aspects of diffusion models, such as the noise schedule or the reverse sampler. In this work, we systematically assess the generalization performance of a diffusion-based speech enhancement model by using multiple speech, noise and binaural room impulse response (BRIR) databases to simulate mismatched acoustic conditions. We also experiment with a noise schedule and a sampler that have not been applied to speech enhancement before. We show that the proposed system substantially benefits from using multiple databases for training, and achieves superior performance compared to state-of-the-art discriminative models in both matched and mismatched conditions. We also show that a Heun-based sampler achieves superior performance at a smaller computational cost compared to a sampler commonly used for speech enhancement.