Learning to Discover: A Generalized Framework for Raga Identification without Forgetting
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.
The following analysis is generated by AI and may contain errors. It is separate from the authors’ abstract and is not peer review.
Research summary
The main contribution of this work is the introduction of a unified framework for Raga identification that effectively balances the discovery of unseen Ragas with the retention of knowledge about known Ragas, addressing the critical issue of catastrophic forgetting in machine learning models. This paper significantly advances the field of music information retrieval by providing a robust solution to a complex problem, showcasing the potential for machine learning to enhance the understanding and classification of intricate musical structures.
Methodology
The proposed methodology introduces a novel framework that integrates supervised and unsupervised contrastive learning within a shared embedding space, effectively addressing the challenges of catastrophic forgetting and enabling the discovery of unseen Ragas. The use of a CNN-LSTM feature extractor followed by a self-attention encoder is a thoughtful choice, leveraging the strengths of both architectures to capture the intricate melodic structures of Indian Art Music. The methodology is well-structured, with clear delineation of the training phases and loss functions, although it could benefit from more detailed descriptions of hyperparameter tuning and the selection process for the temperature parameter in contrastive losses.
Results & evaluation
The experiments are robust, utilizing two distinct datasets (PIM and Saraga) to evaluate the proposed method's performance across known and unseen classes. The results demonstrate significant improvements over baseline methods, particularly in maintaining accuracy for known classes while effectively discovering new Ragas. The evaluation metrics used (ACC, NMI, ARI, and Silhouette Score) are appropriate for assessing clustering performance, although additional qualitative analyses, such as visualizations of the learned embeddings, could further strengthen the findings.
Reproducibility
While the paper mentions that the code will be shared on GitHub, specific implementation details, such as the exact architecture of the CNN-LSTM model and the training configurations, are somewhat lacking. Providing access to the code and detailed instructions would enhance reproducibility. The absence of a demo URL limits the ability for others to interact with the model directly.
Limitations
A notable limitation is the reliance on the assumption that the unlabelled dataset is strictly disjoint from the labelled one, which may not hold in all real-world scenarios. Additionally, the performance on certain Ragas, such as Bhopali and Shuddha-Kalyan, indicates that the model may still struggle with certain tonal similarities, suggesting room for improvement in distinguishing closely related classes.
Broader impact
The proposed framework has significant implications for music information retrieval and could enhance the accessibility of Indian Art Music by enabling better categorization and discovery of Ragas. The approach could also be adapted for other musical traditions, potentially fostering cross-cultural understanding and appreciation of diverse musical forms. The main contribution of this work is the introduction of a unified framework for Raga identification that effectively balances the discovery of unseen Ragas with the retention of knowledge about known Ragas, addressing the critical issue of catastrophic forgetting in machine learning models. This paper significantly advances the field of music information retrieval by providing a robust solution to a complex problem, showcasing the potential for machine learning to enhance the understanding and classification of intricate musical structures.
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