Conference Papers
Permanent URI for this collectionhttps://idr.nitk.ac.in/handle/123456789/28506
Browse
4 results
Search Results
Item DSL approach for development of gaming applications(Springer Verlag service@springer.de, 2016) Vijayakumar, A.; Abhishek, D.; Chandrasekaran, K.This research paper mainly concentrates on introducing DSL(Domain Specific language) approach in developing gaming applications. DSL approach hides the lower level implementation in C, C Sharp, C++, and JAVA and provides abstraction of higher level. The higher level of abstraction provided by the Domain Specific Language approach is error-free and easy to develop. The aim of this paper is to propose an approach to use GaML (Gamification Modelling Language, a form of DSL for gaming) for Unity based complex games efficiently in this paper. The paper doesn’t focus on the How and Whys of the Gaming Modelling Language usage, but rather focuses on the run-time enforcement. At the end of the paper, survey has been made on total lines of code and time invested for coding using a case study. The case study proves that DSL approach of automated code generation is better than manual. © Springer India 2016.Item Estimating multiple physical parameters from speech data(IEEE Computer Society help@computer.org, 2016) Kalluri, S.B.; Vijayakumar, A.; Vijayasenan, D.; Singh, R.In this work, we explore prediction of different physical parameters from speech data. We aim to predict shoulder size and waist size of people from speech data in addition to the conventional height and weight parameters. A data-set with this information is created from 207 volunteers. A bag of words representation based on log magnitude spectrum is used as features. A support vector regression predicts the physical parameters from the bag of the words representation. The system is able to achieve a root mean square error of 6.6 cm for height estimation, 2.6cm for shoulder size, 7.1cm for waist size and 8.9 kg for weight estimation. The results of height estimation is on par with state of the art results. © 2016 IEEE.Item Product review based on optimized facial expression detection(Institute of Electrical and Electronics Engineers Inc., 2017) Chaugule, V.; Abhishek, D.; Vijayakumar, A.; Ramteke, P.B.; Koolagudi, S.G.This paper proposes a method to review public acceptance of products based on their brand by analyzing the facial expression of the customer intending to buy the product from a supermarket or hypermarket. In such cases, facial expression recognition plays a significant role in product review. Here, facial expression detection is performed by extracting feature points using a modified Harris algorithm. The modified Harris algorithm reduced the time complexity of the existing feature extraction Harris Algorithm. A comparison of time complexities of existing algorithms is done with proposed algorithm. The algorithm proved to be significantly faster and nearly accurate for the needed application by reducing the time complexity for corner points detection. © 2016 IEEE.Item Prediction of aesthetic elements in Karnatic music: A machine learning approach(International Speech Communication Association publication@isca-speech.org 4 Rue des Fauvettes - Lous Tourils Baixas 66390, 2018) Rajan, M.; Vijayakumar, A.; Vijayasenan, D.Gamakas, the embellishments and ornamentations used to enhance musical experience, are defining features of Karnatic Music (KM). The appropriateness of using gamaka is determined by aesthetics and is often developed by musicians with experience. Therefore, understanding and modeling gamaka is a significant bottleneck in applications like music synthesis, automatic accompaniment, etc. in the context of KM. To this end, we propose to learn both the presence and the type of gamaka in a data-driven manner using annotated symbolic music. In particular, we explore the efficacy of three classes of features - note-based, phonetic and structural - and train a Random Forest Classifier to predict the existence and the type of gamaka. The observed accuracy is ∼70% for gamaka detection and ∼60% for gamaka classification. Finally, we present an analysis of the features and find that frequency and duration of the neighbouring notes prove to be the most important features. © 2018 International Speech Communication Association. All rights reserved.