2024/07/04 by Yang Xiao, Xiao, Yang, Rohan Kumar Das +1 · 2 citations
Computer Science · #Audio and Speech Processing (eess.AS) #FOS: Computer and information sciences #FOS: Electrical engineering #Neural Networks and Applications #Sound (cs.SD) #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.2407.03661
openalex publication_date 2024/07/04 · openalex created_date 2024/07/09 · openalex updated_date 2026/07/28
Sound source localization (SSL) is essential for many speech-processing applications. Deep learning models have achieved high performance, but often fail when the training and inference environments differ. Adapting SSL models to dynamic acoustic conditions faces a major challenge: catastrophic forgetting. In this work, we propose an exemplar-free continual learning strategy for SSL (CL-SSL) to address such a forgetting phenomenon. CL-SSL applies task-specific sub-networks to adapt across diverse acoustic environments while retaining previously learned knowledge. It also uses a scaling mechanism to limit parameter growth, ensuring consistent performance across incremental tasks. We evaluated CL-SSL on simulated data with varying microphone distances and real-world data with different noise levels. The results demonstrate CL-SSL's ability to maintain high accuracy with minimal parameter increase, offering an efficient solution for SSL applications.