Chathuranga Weeraddana

Charmin Asirimath, Jayampathy Ratnayake, Chathuranga Weeraddana

Computation of the simplicial complexes of a large point cloud often relies on extracting a sample, to reduce the associated computational burden. The study considers sampling critical points of a Morse function associated to a point cloud, to approximate the Vietoris-Rips complex or the witness complex and compute persistence homology. The effectiveness of the novel approach is compared with the farthest point sampling, in a context of classifying human face images into ethnics groups using persistence homology.

Nishal Silva, Chathuranga Weeraddana, Carlo Fischione

Musical onset detection is a key component in any beat tracking system. Existing onset detection methods are based on temporal/spectral analysis, or methods that integrate temporal and spectral information together with statistical estimation and machine learning models. In this paper, we propose a method to localize onset components in music by using the S-transform, and thus, the method is purely based on temporal/spectral data. Unlike the other methods based on temporal/spectral data, which usually rely short time Fourier transform (STFT), our method enables effective isolation of crucial frequency subbands due to the frequency dependent resolution of S-transform. Moreover, numerical results show, even with less computationally intensive steps, the proposed method can closely resemble the performance of more resource intensive statistical estimation based approaches.