让机器识别并聚类印度音乐中从未见过的拉加，突破传统分类局限。
Identification and Clustering of Unseen Ragas in Indian Art Music
- 用不确定性检测识别未知拉加，再通过对比学习聚类。
- 在未知拉加上实现有效聚类，性能受损失函数设计影响显著。
- 适合对开放集音乐分类、跨域音频分析感兴趣的读者。
印度艺术音乐中的拉加分类是一个开放集问题，测试阶段可能遇到未见类别。然而，传统方法常将其视为闭集问题，忽略未知类别的可能性。本文首先采用基于不确定性的分布外（OOD）检测，识别包含已知与未知类别的样本；对于被判定为OOD的音频，进一步应用新颖类别发现（NCD）方法，将其聚类为不同的未知拉加类别。通过利用标注数据信息，并在无标签数据上施加对比学习，实现高效聚类。我们通过详尽分析，揭示了损失函数各组件对聚类性能的影响，并考察了不同开放度设置对NCD任务的影响。
原文摘要 · Abstract (English)
Raga classification in Indian Art Music is an open-set problem where unseen classes may appear during testing. However, traditional approaches often treat it as a closed set problem, rejecting the possibility of encountering unseen classes. In this work, we try to tackle this problem by first employing an Uncertainty-based Out-Of-Distribution (OOD) detection, given a set containing known and unknown classes. Next, for the audio samples identified as OOD, we employ Novel Class Discovery (NCD) approach to cluster them into distinct unseen Raga classes. We achieve this by harnessing information from labelled data and further applying contrastive learning on unlabelled data. With thorough analysis, we demonstrate the influence of different components of the loss function on clustering performance and examine how varying openness affects the NCD task in hand.
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