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20242026
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eess.AS2026

Automatic Detection and Analysis of Singing Mistakes for Music Pedagogy

Sumit Kumar, Suraj Jaiswal, Parampreet Singh +1

The advancement of machine learning in audio analysis has opened new possibilities for technology-enhanced music education. This paper introduces a framework for automatic singing…

eess.AS2026

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

Parampreet Singh, Somya Kumar, Chaitanya Shailendra Nitawe +1

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. Tr…

eess.AS2026

Weakly Supervised Tabla Stroke Transcription via TI-SDRM: A Rhythm-Aware Lattice Rescoring Framework

Rahul Bapusaheb Kodag, Vipul Arora

Tabla Stroke Transcription (TST) is central to the analysis of rhythmic structure in Hindustani classical music, yet remains challenging due to complex rhythmic organization and th…

eess.AS2026

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

Parampreet Singh, Akshay Raina, Sayeedul Islam Sheikh +1

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…

eess.AS2025

Improving Active Learning for Melody Estimation by Disentangling Uncertainties

Aayush Jaiswal, Parampreet Singh, Vipul Arora

Estimating the fundamental frequency, or melody, is a core task in Music Information Retrieval (MIR). Various studies have explored signal processing, machine learning, and deep-le…

eess.AS2025

Identification and Clustering of Unseen Ragas in Indian Art Music

Parampreet Singh, Adwik Gupta, Aakarsh Mishra +1

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 proble…