Paper ID: 2501.04116

Artifact-free Sound Quality in DNN-based Closed-loop Systems for Audio Processing

chuan Wen, Guy Torfs, Sarah Verhulst

Recent advances in deep neural networks (DNNs) have significantly improved various audio processing applications, including speech enhancement, synthesis, and hearing aid algorithms. DNN-based closed-loop systems have gained popularity in these applications due to their robust performance and ability to adapt to diverse conditions. Despite their effectiveness, current DNN-based closed-loop systems often suffer from sound quality degradation caused by artifacts introduced by suboptimal sampling methods. To address this challenge, we introduce dCoNNear, a novel DNN architecture designed for seamless integration into closed-loop frameworks. This architecture specifically aims to prevent the generation of spurious artifacts. We demonstrate the effectiveness of dCoNNear through a proof-of-principle example within a closed-loop framework that employs biophysically realistic models of auditory processing for both normal and hearing-impaired profiles to design personalized hearing aid algorithms. Our results show that dCoNNear not only accurately simulates all processing stages of existing non-DNN biophysical models but also eliminates audible artifacts, thereby enhancing the sound quality of the resulting hearing aid algorithms. This study presents a novel, artifact-free closed-loop framework that improves the sound quality of audio processing systems, offering a promising solution for high-fidelity applications in audio and hearing technologies.

Submitted: Jan 7, 2025