A Multi-Branch Hierarchy-Aware Framework for Heterogeneous Audio Classification

2026-07-02

AI Summary

This work addresses the challenges of hierarchical consistency and fine-grained sub-label classification in heterogeneous audio within the Broad Sound Taxonomy (BST). To tackle these issues, the authors propose a unified framework that integrates multi-branch heterogeneous modeling, a hierarchy-aware classifier, and KNN-based post-processing. Leveraging CLAP audio-text representations alongside complementary acoustic features such as log-STFT, the method enhances fine-grained classification accuracy while preserving hierarchical structural constraints. Evaluated on BSD10k-v1.2, the single-model variant achieves a hierarchical F1 score of 80.84%, and an ensemble system further improves this to 81.25%, substantially outperforming existing approaches.

Abstract

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.

Problem

• heterogeneous audio classification
• Broad Sound Taxonomy
• hierarchical consistency
• audio taxonomy
• DCASE challenge

Innovation

• multi-branch
• hierarchy-aware
• CLAP-based representation
• KNN post-processing
• heterogeneous audio classification