Work place: Department of computer science and Engineering, GITAM University, Andhra Pradesh, India
Research Interests: Emotion Recognition, Digital Forensics, Data Mining, Speech Recognition, Image Processing, Software Engineering
Dr. Srinivas Yarramalle was awarded M. Tech (Computer Science & Technology) from Andhra University -1999. He was awarded Doctorate in Computer Science with Specialization in Image Processing from Acharya Nagarjuna University, Guntur.- 21-1-2008. He received Best Teacher Award from JNTU University, Sept-5-2010 and from B.C. Corporation in 2005. Dr. Y Srinivas is currently working as a Professor in the Department of computer science and engineering, GITAM University. Dr. Srinivas does research in Computer Engineering. He prepared 4 Monographs for School of Correspondence Courses, Andhra University. He received SASTRA Award from Vignan‘ s Institute of Information Technology, 10-Jan-2008, for Research publications. Received SASTRA Award from Vignan‘s Institute of Information Technology, 10-Jan- 2009, for Research publications.
DOI: https://doi.org/10.5815/ijieeb.2015.03.06, Pub. Date: 8 May 2015
The present day users navigate more using electronic gadgets, interacting with social networking sites and retrieving the images of interest from the information groups or similar groups. Most of the retrievals techniques are not much effective due to the semantic gap. Many models have been discussed for effective retrievals of the images based on feature extraction, label based and semantic rules. However effective retrievals of images are still a challenging task, model based techniques together with semantic attributes provide alternatives for efficient retrievals. This article is developed with the concepts of Generalized Gaussian Mixture Models and Semantic attributes. Flicker dataset is considered to experiment the model and efficiency is measured using Precision and Recall.[...] Read more.
DOI: https://doi.org/10.5815/ijmecs.2015.01.08, Pub. Date: 8 Jan. 2015
Today major section of automatic speaker verification (ASV) research is focused on multiple objectives like optimization of feature subset and minimization of Equal Error Rate (EER). As such, numerous systems for feature dimension reduction are proposed. This includes framework coaching and testing analysis for every feature set that could be a time esurient trip. Because of its significance, the issue of feature selection has been researched by numerous scientists. In this paper, a new feature subset selection procedure is presented. Hybrid of Ant Colony and Artificial Bee Colony optimized the feature subset over 85% thereby decreased the computational complexity of ASV. Additionally an external record is maintained to store non-dominated solution vectors for which concept of Pareto dominance is used. An overall optimization of 87% is achieved thereby improved the recognition rate of ASV.[...] Read more.
DOI: https://doi.org/10.5815/ijigsp.2014.07.08, Pub. Date: 8 Jun. 2014
Speech Processing has been developed as one of the vital provision region of Digital Signal Processing. Speaker recognition is the methodology of immediately distinguishing who is talking dependent upon special aspects held in discourse waves. This strategy makes it conceivable to utilize the speaker's voice to check their character and control access to administrations, for example voice dialing, data administrations, voice send, and security control for secret information.
A review on speaker recognition and emotion recognition is performed based on past ten years of research work. So far iari is done on text independent and dependent speaker recognition. There are many prosodic features of speech signal that depict the emotion of a speaker. A detailed study on these issues is presented in this paper.
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