提升复杂音频分类的层次化准确率，兼顾顶层分类一致性。
A Multi-Branch Hierarchy-Aware Framework for Heterogeneous Audio Classification
- 用多分支结构分别处理不同声学特征，增强模型表达能力。
- 单模型在测试集上达80.84%层次化F1，集成后最高达81.25%。
- 适合做音频分类任务且关注类别层级关系的研究者参考。
本文介绍我们在DCASE 2026挑战赛任务1中的系统，目标是根据广义声音分类体系（BST）对异构音频进行分类，要求既实现精准的二级分类，又保持与顶层分类体系的一致性。系统基于CLAP音频-文本表征，通过三个策略改进：使用筛选后的BSD35k子集扩充训练集，采用针对特定特征的分支增强声学建模，以及利用层次感知分类器和基于KNN的后处理优化预测结果。在所考虑的声学特征中，对数STFT分支表现最优。结合KNN后处理，最佳单模型在BSD10k-v1.2数据集上达到80.84%的层次化F1得分（与基线评估协议一致）。通过融合具备互补声学特征和分类头的模型构建集成系统，进一步获得81.25%和81.18%的层次化F1得分。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。