A Multi-Branch Hierarchy-Aware Framework for Heterogeneous Audio Classification
This work improves hierarchical audio classification for the DCASE challenge, but the gains are incremental over existing methods.
The authors tackle heterogeneous audio classification using the Broad Sound Taxonomy, achieving a hierarchical F1 score of 80.84% with a single system and up to 81.25% with ensembles, outperforming the baseline.
This technical report describes our system for Task 1 of the DCASE 2026 Challenge, which aims to classify heterogeneous audio recordings according to the Broad Sound Taxonomy (BST). The task requires both accurate second-level prediction and consistency with the top-level taxonomy. Our system is built on CLAP-based audio-text representations and is improved along three strategies: expanding the training set with a filtered subset of BSD35k, enhancing acoustic modeling with feature-specific branches, and refining predictions using hierarchy-aware classifiers and KNN-based post-processing. Among the acoustic features considered, the log-STFT branch provides the strongest single-model performance. With KNN-based post-processing, our best single system achieves a hierarchical F1 score (Hier. F1) of 80.84% on the BSD10k-v1.2 set under the same evaluation protocol as the baseline. We further construct ensemble systems by combining models with complementary acoustic features and classification heads, achieving Hier. F1 scores of 81.25% and 81.18%, respectively.