arXiv subjects

Sayeedul Islam Sheikh

Publications and source records attributed to Sayeedul Islam Sheikh.

2 recordsLinked to original sources

arXiv2026

Intercoupling of Segregation and Rheology in Spatially Developing Granular Chute Flows

We investigate the flow and segregation of binary granular mixtures with density differences down a long chute using a continuum framework that couples a particle-force-based segregation model with an inertial-number-based local rheology. The steady-state momentum and convection-diffusion-segregation equations are solved simultaneously, explicitly accounting for the two-way coupling between segregation and flow. Predicted concentration and velocity fields at different streamwise locations are validated against representative DEM simulations. The validated model is then used to examine the influence of density ratio, mixture composition, and chute inclination on segregation over a wide range of conditions. The chute length required to achieve fully developed segregation is quantified and compared with the development length for monodisperse granular flow. At low density ratios and/or low inclinations, segregation develops over much longer distances than the velocity field. In contrast, at higher inclinations and larger density contrasts, the two length scales become comparable, demonstrating that neglecting flow development can significantly underestimate segregation evolution.

cond-mat.soft↗

arXiv2026

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

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.

eess.AS↗