A
Anil Kumar Sao
Researcher at Indian Institute of Technology Mandi
Publications - 85
Citations - 835
Anil Kumar Sao is an academic researcher from Indian Institute of Technology Mandi. The author has contributed to research in topics: Sparse approximation & Face (geometry). The author has an hindex of 15, co-authored 79 publications receiving 696 citations. Previous affiliations of Anil Kumar Sao include Indian Institute of Technology Madras.
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Journal ArticleDOI
Solar radiation forecasting with multiple parameters neural networks
TL;DR: The review discloses the incredible view of using the neural networks in solar forecast and summarizes the major applications of eight well recognized and often used neural network models of which the last two are custom based.
Proceedings ArticleDOI
A syllable-based framework for unit selection synthesis in 13 Indian languages
Hemant A. Patil,Tanvina B. Patel,Nirmesh J. Shah,Hardik B. Sailor,Raghava Krishnan,G. R. Kasthuri,T. Nagarajan,Lilly Christina,Naresh Kumar,Veera Raghavendra,Surekha Kishore,S. R. M. Prasanna,Nagaraj Adiga,Sanasam Ranbir Singh,Konjengbam Anand,Pranaw Kumar,Bira Chandra Singh,S. L. Binil Kumar,T. G. Bhadran,T. Sajini,Arup Saha,T. K. Basu,K. Sreenivasa Rao,N. P. Narendra,Anil Kumar Sao,Rajesh Kumar,Pranhari Talukdar,Purnendu Acharyaa,Somnath Chandra,Swaran Lata,Hema A. Murthy +30 more
TL;DR: A consortium effort on building text to speech (TTS) systems for 13 Indian languages using the same common framework and the TTS systems are evaluated using Mean Opinion Score (DMOS) and Word Error Rate (WER).
Journal ArticleDOI
Noise adaptive super-resolution from single image via non-local mean and sparse representation
TL;DR: A robust super-resolution algorithm which adapts itself based on the noise-level in the image, which demonstrates better efficacy for optical and range images under different types and strengths of noise.
Journal ArticleDOI
Deep-Sparse-Representation-Based Features for Speech Recognition
TL;DR: This paper proposes to use a multilevel decomposition (having multiple layers), also known as the deep sparse representation (DSR), to derive a feature representation for speech recognition, and reveals that the representations obtained at different sparse layers of the proposed DSR model have complimentary information.
Proceedings ArticleDOI
CNN based segmentation of nuclei in PAP-smear images with selective pre-processing
TL;DR: This paper proposes a new approach for segmentation of nuclei based on selective pre-processing and then passing the image patches to respective deep CNN (trained with/without pre-processed images) for pixel-wise 3 class labelling as nucleus, edge or background.