6 citations · 7 across the 17 of their papers we have counts for
18 papers
Confidence-Guided Markov Weighting for Semi-Supervised Tabla Stroke Transcription
Rahul Bapusaheb Kodag, Vipul Arora
Tabla Stroke Transcription (TST) converts tabla audio into symbolic stroke sequences, but the scarcity of annotated recordings makes fully supervised training challenging. We propo…
Weakly Supervised Tabla Stroke Transcription via an Adaptive Dynamic Rhythm Language Model (ADRM)
Rahul Bapusaheb Kodag, Vipul Arora
Tabla Stroke Transcription (TST) is central to the analysis of rhythmic structure in Hindustani music, yet it remains challenging due to complex and dynamic rhythmic organization a…
: Margin-Controlled Confidence estimation for reliable Music Information Retrieval
Parampreet Singh, Anushka Singh, Sumit Kumar +1
Deep neural networks are often overconfident, assigning high confidence even to incorrect predictions. Consequently, users lack a reliable signal for deciding when a prediction can…
wav2tok 2.0: Scalable Audio Tokenization Maintaining Explicit Pairwise Token Alignment for Efficient Audio Retrieval
Adhiraj Banerjee, Vipul Arora
Learning discrete speech representations that preserve similarity across variable-length utterances is central to query-by-example spoken term detection (QbE-STD). While wav2tok in…
PairAlign: A Framework for Autoregressive Tokenization via Self-Alignment with Applications to Audio Tokenization
Adhiraj Banerjee, Vipul Arora
Modern learning systems represent perceptual signals with continuous vectors, but comparison, retrieval, memory, alignment, and reasoning are often symbolic. In language, tokens pr…
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…
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…
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,…
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…
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…
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…
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…
Recognizing Ornaments in Vocal Indian Art Music with Active Annotation
Sumit Kumar, Parampreet Singh, Vipul Arora
Ornamentations, embellishments, or microtonal inflections are essential to melodic expression across many musical traditions, adding depth, nuance, and emotional impact to performa…
Meta-learning-based percussion transcription and identification from low-resource audio
Rahul Bapusaheb Kodag, Vipul Arora
This study introduces a meta-learning-based approach for low-resource Tabla Stroke Transcription (TST) and identification in Hindustani classical music. Using Model-Ag…
Dementia Detection using Multi-modal Methods on Audio Data
Saugat Kannojia, Anirudh Praveen, Danish Vasdev +5
Dementia is a neurodegenerative disease that causes gradual cognitive impairment, which is very common in the world and undergoes a lot of research every year to prevent and cure i…
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…
A System for Automatic Identification and Generation
Rahul Bapusaheb Kodag, Himanshu Jindal, Vipul Arora
In Hindustani classical music, the tabla plays an important role as a rhythmic backbone and accompaniment. In applications like computer-based music analysis, learning singing, and…
Explainable Deep Learning Analysis for Raga Identification in Indian Art Music
Parampreet Singh, Vipul Arora
Raga identification is an important problem within the domain of Indian Art music, as Ragas are fundamental to its composition and performance, playing a crucial role in music retr…