From-Scratch Audio Classification with Multi-Branch Feature Fusion: Comparing CNN, Transformer, and Mamba - Giacomini, Anderson H.; Magalhães, André M. S.; Sousa, Elaine P. M. (Anais do Simpósio Brasileiro de Banco de Dados (SBBD), 2026)
From-scratch audio classification on weakly labeled data is essential for resource-constrained and domain-specific settings where large pretrained models are unavailable or prohibitively costly. We adopt multi-branch feature fusion as the core approach and compare CNN, Inception, Transformer, and Mamba architectures on the AudioSet balanced training split. Multi-branch fusion (per-feature encoders, merged before the head) improves all four architectures by 17–33% mAP. Five-fold CV shows Inception significantly outperforms the Transformer; Transformer and Mamba reach comparable multi-branch performance (p = 0.19), with preliminary results suggesting feature fusion may be at least as important as sequence model choice in this regime.