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arXiv · 2603.24385

ArrayDPS-Refine: Generative Refinement of Discriminative Multi-Channel Speech Enhancement

Zhongweiyang Xu·Ashutosh Pandey·Juan Azcarreta·Zhaoheng Ni·Sanjeel Parekh·Buye Xu
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Abstract

Multi-channel speech enhancement aims to recover clean speech from noisy multi-channel recordings. Most deep learning methods employ discriminative training, which can lead to non-linear distortions from regression-based objectives, especially under challenging environmental noise conditions. Inspired by ArrayDPS for unsupervised multi-channel source separation, we introduce ArrayDPS-Refine, a method designed to enhance the outputs of discriminative models using a clean speech diffusion prior. ArrayDPS-Refine is training-free, generative, and array-agnostic. It first estimates the noise spatial covariance matrix (SCM) from the enhanced speech produced by a discriminative model, then uses this estimated noise SCM for diffusion posterior sampling. This approach allows direct refinement of any discriminative model's output without retraining. Our results show that ArrayDPS-Refine consistently improves the performance of various discriminative models, including state-of-the-art waveform and STFT domain models. Audio demos are provided at https://xzwy.github.io/ArrayDPSRefineDemo/.

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Zhongweiyang Xu, Ashutosh Pandey, Juan Azcarreta, Zhaoheng Ni, Sanjeel Parekh, Buye Xu. 2026-03-25. ArrayDPS-Refine: Generative Refinement of Discriminative Multi-Channel Speech Enhancement. https://arxiv.org/abs/2603.24385

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