A Novel Dual-Branch Bi-Mamba Architecture for Acoustic Cough Segmentation

Precise temporal segmentation of acoustic cough signals is critical for digital health, yet existing literature predominantly focuses on simple event detection rather than exact boundary delineation. To bridge this gap, we introduce a comprehensive benchmarking framework specifically designed to systematically evaluate continuous boundary detection performance using modern deep learning architectures. Built upon this evaluation paradigm, we propose a novel Dual-Branch Bi-Mamba architecture that effectively integrates the local morphological feature extraction capabilities of a 2D U-Net with the long-range sequential modeling power of 1D Bidirectional State-Space Models (SSMs). Evaluated on the clinical DKPNet41 dataset, the proposed compact 0.54-million-parameter model achieved an F1-Score of 87.66% while reducing offset boundary error by over 50%. Operating 56× faster than real time on a standard CPU, this study establishes a reliable evaluation framework for precise boundary segmentation and provides a computationally efficient architectural solution for high-resolution automated acoustic signal processing.