Diff-PLC: A Diffusion-Based Approach For Effective Packet Loss Concealment

Abstract

We introduce diffusion-based packet loss concealment (DiffPLC), a novel approach designed to improve speech quality in the presence of packet losses for speech transmission. Derived from the foundation of a diffusion-based neural vocoder, the Diff-PLC introduces a crucial modification and supplementary concepts for the reconstruction of lost packets. A key aspect of the Diff-PLC involves integrating a feature-wise linear modulation layer into the diffusion model, facilitating the seamless incorporation of a conditioning feature. Furthermore, the Diff-PLC leverages packet loss embedding as an additional conditioning feature which significantly assists the diffusion model in restoring lost packets. The proposed model is evaluated using the blind test set of the INTERSPEECH 2022 PLC challenge, demonstrating the considerable restoration capabilities of Diff-PLC across various reference-free and reference-based metrics, including PLCMOS, PESQ, STOI, and NISQA.