Author
Anil Kumar Sao
Other affiliations: Indian Institute of Technology Madras
Bio: 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.
Papers published on a yearly basis
Papers
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01 Jan 2014
TL;DR: The tasks consisted of data from six Indian languages and used the speech data provided as well as the corresponding text transcriptions in UTF-8 to build synthetic voices, which were then evaluated by means of listening tests.
Abstract: The Blizzard challenge 2014 was the tenth annual Blizzard challenge organized by the following group of institutions : IIIT Hyderabad, IIT Madras, DAIICT, SSN College of Engineering, IIT Mandi and IIT Guwahati with support and collaboration from DeitY, Government of India. This paper describes the tasks in the Blizzard challenge 2014. The tasks consisted of data from six Indian languages : Assamese, Gujarati, Hindi, Rajasthani, Tamil and Telugu. Seven participants from around the world used the speech data provided as well as the corresponding text transcriptions in UTF-8, to build synthetic voices, which were then evaluated by means of listening tests. Index Terms: Blizzard challenge, Speech synthesis, Evaluation of synthetic speech
15 citations
TL;DR: Both raw speech samples and mel frequency cepstral coefficients are used as an initial representation for feature extraction and a transformation function known as weighted decomposition (WD) of principal components is used to emphasize the discriminative information present in the PCA-based dictionary.
15 citations
04 Feb 2009
TL;DR: A representation based on the phase of analytic image is proposed to address the issue of illumination variation in face recognition task by using trigonometric functions of phase and template matching.
Abstract: A representation based on the phase of analytic image is proposed to address the issue of illumination variation in face recognition task. The problem of unwrapping in the computation of analytic phase is avoided by using trigonometric functions of phase. Template matching is used to compare the functions of analytic phase for face recognition. For template matching, the functions of the analytic phase are compressed using eigenanalysis. Performance of the face recognition is improved by using weights derived from the eigenvalues in the template matching.
14 citations
25 Aug 2013
TL;DR: Experimental results show that the proposed approach can be an alternative to the existing approaches for speech enhancement and modified to ensure the speech enhancement across the varying segments of speech signal.
Abstract: This paper proposes an approach based on the compressed sensing for speech enhancement. It attempts to exploit the fact that it is relatively easy to find sparse representation for speech signal but the same cannot hold true for noise. Thus for a given speech signal a sparse vector is derived which extracts the speech signal from the given noisy speech signal. Approach is further modified to ensure the speech enhancement across the varying segments of speech signal, which is present due to language, emotion and speech uttered by a person. Experimental results show that the proposed approach can be an alternative to the existing approaches for speech enhancement.
14 citations
Proceedings Article•
27 Sep 2012TL;DR: The experimental results suggest that the proposed approach addresses the issue of sufficiency of training data efficiently, as it provides multiple partial evidences, which are combined to enhance the face recognition performance.
Abstract: Sparse representation based classification (SRC) successfully addresses the problem of face recognition under various illumination and occlusion conditions, if sufficient training images are given. This paper discusses the significance of dictionary in sparse coding based face recognition. We primarily address the problem of sufficiency of training data in various illumination conditions. The dictionary is generated using a lower dimensional representation of image, which emphasizes the subject specific unique information of the face image. This representation is called weighted decomposition (WD) face image, because it attempts to give more weightage to unique information of face image. The effect of illumination in computation of WD face image is reduced using edginess based representation of image, which is derived using one-dimensional (1-D) processing of image. 1-D processing provides multiple partial evidences, which are combined to enhance the face recognition performance. The experimental results suggest that the proposed approach addresses the issue of sufficiency of training data efficiently.
13 citations
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TL;DR: In this article, an extensive review on recent advancements in the field of solar photovoltaic power forecasting is presented, which aims to analyze and compare various methods of solar PV power forecasting in terms of characteristics and performance.
539 citations
TL;DR: An attempt has been made to scrutinize the applications of artificial neural network (ANN) as an intelligent system-based method for optimizing and the prediction of different solar energy devices’ performance.
389 citations
TL;DR: This paper presents a preliminary study on how to review solar irradiance and photovoltaic power forecasting using text mining, which serves as the first part of a forthcoming series of text mining applications in solar forecasting.
348 citations
TL;DR: To bridge the gap between theory and practicality of CS, different CS acquisition strategies and reconstruction approaches are elaborated systematically in this paper.
Abstract: Compressive Sensing (CS) is a new sensing modality, which compresses the signal being acquired at the time of sensing. Signals can have sparse or compressible representation either in original domain or in some transform domain. Relying on the sparsity of the signals, CS allows us to sample the signal at a rate much below the Nyquist sampling rate. Also, the varied reconstruction algorithms of CS can faithfully reconstruct the original signal back from fewer compressive measurements. This fact has stimulated research interest toward the use of CS in several fields, such as magnetic resonance imaging, high-speed video acquisition, and ultrawideband communication. This paper reviews the basic theoretical concepts underlying CS. To bridge the gap between theory and practicality of CS, different CS acquisition strategies and reconstruction approaches are elaborated systematically in this paper. The major application areas where CS is currently being used are reviewed here. This paper also highlights some of the challenges and research directions in this field.
334 citations
TL;DR: Overall, this review provides preliminary guidelines, research gaps and recommendations for developing a better and more user-friendly DG energy planning optimisation tool.
Abstract: An overview of numerical and mathematical modelling-based distributed generation (DG) system optimisation techniques is presented in this review paper. The objective is to compare different aspects of these two broad classes of DG optimisation techniques, explore their applications, and identify potential research directions from reviewed studies. Introductory descriptions of general electrical power system and DG system are first provided, followed by reviews on renewable resource assessment, load demand analysis, model formulation, and optimisation techniques. In renewable resource assessment model review, uncertain solar and wind energy resources are emphasised whereas applications of forecasting models have been highlighted based on their prediction horizons, computational power requirement, and training data intensity. For DG optimisation framework, (solar, wind and tidal) power generator, energy storage and energy balance models are discussed; in optimisation technique section, both numerical and mathematical modelling optimisation methods are reviewed, analysed and criticised with recommendations for their improvements. In overall, this review provides preliminary guidelines, research gaps and recommendations for developing a better and more user-friendly DG energy planning optimisation tool.
221 citations