用分层模型提升小众乐器识别准确率
A Hierarchical Deep Learning Approach for Minority Instrument Detection
- 基于霍恩博斯特尔-萨克斯分类体系构建分层识别框架
- 在MedleyDB数据集上实现更稳定的粗粒度乐器检测
- 适合音乐信息检索与小众乐器分析场景
音频片段中的乐器活动识别在音乐信息检索中至关重要，对音乐目录管理与发现具有重要意义。以往深度学习研究多集中于数据丰富的乐器类别。近期研究表明，即使在乐器层级标注有限的情况下，分层分类仍可有效检测管弦乐中的乐器活动。本文基于霍恩博斯特尔-萨克斯分类体系，在以多样性和丰富性著称的MedleyDB数据集上评估了分层分类系统。提出多种将分层结构融入模型的策略，并测试了一类新型分层音乐预测模型。研究展示了通过连接细粒度识别与群组级识别，实现更可靠的粗粒度乐器检测，为该领域进一步发展奠定基础。
原文摘要 · Abstract (English)
Identifying instrument activities within audio excerpts is vital in music information retrieval, with significant implications for music cataloging and discovery. Prior deep learning endeavors in musical instrument recognition have predominantly emphasized instrument classes with ample data availability. Recent studies have demonstrated the applicability of hierarchical classification in detecting instrument activities in orchestral music, even with limited fine-grained annotations at the instrument level. Based on the Hornbostel-Sachs classification, such a hierarchical classification system is evaluated using the MedleyDB dataset, renowned for its diversity and richness concerning various instruments and music genres. This work presents various strategies to integrate hierarchical structures into models and tests a new class of models for hierarchical music prediction. This study showcases more reliable coarse-level instrument detection by bridging the gap between detailed instrument identification and group-level recognition, paving the way for further advancements in this domain.
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