AVSE-Pruner: Filter Pruning of Audio-Visual Speech Enhancement System using Multi-objective Binary Particle Swarm Optimization

This paper optimizes filter pruning as a constrained multiobjective optimization problem using a new binary multiobjective particle swarm optimization with dynamic learning strategies (AVSE-Pruner).AVSE-Pruner aims to balance network performance and computational cost by incorporating dynamic learning strategies to adjust search behavior.Applying AVSE-Pruner, we pruned the baseline model for the Audio-Visual Speech Enhancement (AVSE) Challenge, which enhances speech intelligibility in noisy environments using audio and visual inputs.Our pruned model maintains high performance while significantly reducing computational burden, demonstrating its suitability for real-time embedded applications.