Learning to Discover: A Generalized Framework for Raga Identification without Forgetting

TL;DR

This paper introduces a unified learning framework for Raga identification in Indian Art Music that effectively recognizes both known and unseen Ragas by leveraging labeled and unlabeled data, overcoming limitations of traditional models.

Contribution

It presents a novel generalized framework that discovers unseen Ragas without forgetting previously learned ones, improving over existing NCD-based methods.

Findings

Outperforms previous NCD-based pipeline in discovering unseen Ragas

Successfully categorizes known, unseen, and all Raga classes

Demonstrates effectiveness on benchmark datasets

Abstract

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.