activity
20192021
most citedUnsupervised Deep Metric Learning via Orthogonality based Probabilistic Loss

22 citations · 23 across the 4 of their papers we have counts for

collaborators

8 papers

eess.AS20211 cited

Unsupervised Domain Adaptation in Speech Recognition using Phonetic Features

Rupam Ojha, C Chandra Sekhar

Automatic speech recognition is a difficult problem in pattern recognition because several sources of variability exist in the speech input like the channel variations, the input m…

cs.LG2021

Semi-Supervised Metric Learning: A Deep Resurrection

Ujjal Kr Dutta, Mehrtash Harandi, Chellu Chandra Sekhar

Distance Metric Learning (DML) seeks to learn a discriminative embedding where similar examples are closer, and dissimilar examples are apart. In this paper, we address the problem…

eess.AS2021

Front-end Diarization for Percussion Separation in Taniavartanam of Carnatic Music Concerts

Nauman Dawalatabad, Jilt Sebastian, Jom Kuriakose +3

Instrument separation in an ensemble is a challenging task. In this work, we address the problem of separating the percussive voices in the taniavartanam segments of Carnatic music…

eess.AS2020

Novel Architectures for Unsupervised Information Bottleneck based Speaker Diarization of Meetings

Nauman Dawalatabad, Srikanth Madikeri, C. Chandra Sekhar +1

Speaker diarization is an important problem that is topical, and is especially useful as a preprocessor for conversational speech related applications. The objective of this paper…

cs.CV202022 cited

Unsupervised Deep Metric Learning via Orthogonality based Probabilistic Loss

Ujjal Kr Dutta, Mehrtash Harandi, Chellu Chandra Sekhar

Metric learning is an important problem in machine learning. It aims to group similar examples together. Existing state-of-the-art metric learning approaches require class labels t…

cs.CV2020

Affinity guided Geometric Semi-Supervised Metric Learning

Ujjal Kr Dutta, Mehrtash Harandi, Chellu Chandra Sekhar

In this paper, we revamp the forgotten classical Semi-Supervised Distance Metric Learning (SSDML) problem from a Riemannian geometric lens, to leverage stochastic optimization with…