Learning from Limited Labels: Transductive Graph Label Propagation for Indian Music Analysis

TL;DR

This paper presents a graph-based semi-supervised learning method using label propagation to efficiently label large unlabeled Indian music datasets, improving annotation quality and reducing manual effort.

Contribution

It introduces a transductive graph label propagation approach for Indian music analysis, effectively leveraging limited labels to annotate large audio collections.

Findings

LP reduces labeling effort significantly

Higher annotation quality than baseline methods

Applicable to Raga identification and Instrument classification

Abstract

Supervised machine learning frameworks rely on extensive labeled datasets for robust performance on real-world tasks. However, there is a lack of large annotated datasets in audio and music domains, as annotating such recordings is resource-intensive, laborious, and often require expert domain knowledge. In this work, we explore the use of label propagation (LP), a graph-based semi-supervised learning technique, for automatically labeling the unlabeled set in an unsupervised manner. By constructing a similarity graph over audio embeddings, we propagate limited label information from a small annotated subset to a larger unlabeled corpus in a transductive, semi-supervised setting. We apply this method to two tasks in Indian Art Music (IAM): Raga identification and Instrument classification. For both these tasks, we integrate multiple public datasets along with additional recordings we acquire from Prasar Bharati Archives to perform LP. Our experiments demonstrate that LP significantly reduces labeling overhead and produces higher-quality annotations compared to conventional baseline methods, including those based on pretrained inductive models. These results highlight the potential of graph-based semi-supervised learning to democratize data annotation and accelerate progress in music information retrieval.