Zijian Sun1,2, Yaqian Li3,4,*, Haoran Liu1,2, Haibin Li3,4, Wenming Zhang3,4
CMC-Computers, Materials & Continua, Vol.83, No.2, pp. 3187-3210, 2025, DOI:10.32604/cmc.2025.061920
- 16 April 2025
Abstract In recent years, audio pattern recognition has emerged as a key area of research, driven by its applications in human-computer interaction, robotics, and healthcare. Traditional methods, which rely heavily on handcrafted features such as Mel filters, often suffer from information loss and limited feature representation capabilities. To address these limitations, this study proposes an innovative end-to-end audio pattern recognition framework that directly processes raw audio signals, preserving original information and extracting effective classification features. The proposed framework utilizes a dual-branch architecture: a global refinement module that retains channel and temporal details and a multi-scale embedding… More >