提出新框架，让模型识别未知拉格且不遗忘已知拉格。
Learning to Discover: A Generalized Framework for Raga Identification without Forgetting
- 结合有标签和无标签音频，统一学习发现新拉格
- 在基准数据集上同时准确识别已知、未知拉格
- 适合需要持续学习的音乐识别任务
印度古典音乐（IAM）中的拉格识别因大量罕见拉格未被训练数据覆盖而极具挑战。传统分类模型假设类别封闭，难以识别或合理归类新拉格。现有方法虽尝试处理未见拉格，但存在灾难性遗忘问题，导致旧知识丢失。本文提出一种统一学习框架，利用有标签与无标签音频，使模型既能发现对应未见拉格的连贯类别，又能保留对已知拉格的记忆。在标准拉格识别数据集上的实验表明，该方法在识别已知、未见及全部拉格类别上均表现优异，优于先前基于NCD的流水线，为IAM任务中的表示学习提供了新思路。
原文摘要 · Abstract (English)
Raga identification in Indian Art Music (IAM) remains challenging due to the presence of numerous rarely performed Ragas that are not represented in available training datasets. Traditional classification models struggle in this setting, as they assume a closed set of known categories and therefore fail to recognise or meaningfully group previously unseen Ragas. Recent works have tried categorizing unseen Ragas, but they run into a problem of catastrophic forgetting, where the knowledge of previously seen Ragas is diminished. To address this problem, we adopt a unified learning framework that leverages both labeled and unlabeled audio, enabling the model to discover coherent categories corresponding to the unseen Ragas, while retaining the knowledge of previously known ones. We test our model on benchmark Raga Identification datasets and demonstrate its performance in categorizing previously seen, unseen, and all Raga classes. The proposed approach surpasses the previous NCD-based pipeline even in discovering the unseen Raga categories, offering new insights into representation learning for IAM tasks.
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