activity
20242026
collaborators

10 papers

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…

astro-ph.CO2026

TORRCH: Tomographic reconstruction of the reionization of cosmic hydrogen with Ly emitters and non-Ly-selected galaxies

Soumak Maitra, Girish Kulkarni, Vipul Arora +5

Tomographic reconstruction of reionization is a long-sought goal. It would move the field beyond global summary statistics, such as the volume-averaged ionised fraction, to direct,…

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…

cs.SD2025

Continual Learning for Singing Voice Separation with Human in the Loop Adaptation

Ankur Gupta, Anshul Rai, Archit Bansal +1

Deep learning-based works for singing voice separation have performed exceptionally well in the recent past. However, most of these works do not focus on allowing users to interact…

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…