Advanced Methods in Regional Speech Enhancement and Far-to-Near-Field Transformation (video)

Enhancing speech signals in complex acoustic environments remains a critical challenge in audio processing. The presenters recent work presents several innovative approaches to tackle key issues in this domain. They introduced a novel audio zooming technique based on deep learning, shifting from traditional direction-based beamforming to a user-defined, adjustable 3D region for sound capture. This advancement enables precise and flexible audio acquisition, supporting real-time applications such as remote conferencing, education, and live streaming. Building on the concept of region-based sound capture, they further aimed to transform the captured far-field audio into near-field quality. Leveraging real-world acoustic data, they proposed a novel framework that integrates a Schrödinger Bridge-based diffusion model with generative adversarial networks. This approach achieved state-of-the-art performance in reducing noise and reverberation, significantly improving speech quality. The methodology establishes a new benchmark for real-world far-field to near-field enhancement, providing interpretable insights into model behavior and the spectral recovery characteristics of generative versus predictive approaches. These advancements provide effective solutions to long-standing challenges in speech processing, enabling high-quality audio experiences in diverse applications.