Browsing by Author "Murthy, Y.V.S."

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Academic Curriculum Load Balancing using GA
(2019) Chakradhar, M.; Charan, M.S.; Sai, R.U.; Kunal, M.; Murthy, Y.V.S.; Koolagudi, S.G.
In the paper, we propose an algorithm using genetic alogithm to find out the optimal solution for the academic load balancing problem. The load balancing problem is to optimize the load of credits per semester in an academic curriculum. In the proposed method, we try to distribute the course load as evenly as possible so that the deviation from the mean credit load per each semester is as minimal as possible. The objective function is to distribute the credit load among all the semesters evenly such that the deviation from the mean credits per semester is minimal. The proposed approach explores the solution space using only mutation operators and does not operate using crossover as the solutions obtained using cross over does not create any newer and better solutions in the solution space.The algorithm is applied on three data sets and the results are compared with the solutions obtained using the existing approaches. The results obtained using the state of the art solution are either better than approaches or on par with the state of art optimal solutions. The solution set obtained using the proposed approach is well spread out through out all the periods and all the periods contain almost mean number of credits. � 2019 IEEE.
Classification of vocal and non-vocal regions from audio songs using spectral features and pitch variations
(2015) Murthy, Y.V.S.; Koolagudi, S.G.
In this work, an effort has been made to identify vocal and non-vocal regions from a given song using signal processing techniques and machine learning algorithm. Initially spectral features like mel-frequency cepstral coefficients (MFCCs) are used to develop the baseline system. Statistical values of pitch, jitter and shimmer are considered to improve performance of the system. Artificial neural networks (ANNs) are used to capture the characteristics of vocal and non-vocal segments of the songs. The experiment is conducted on 60 vocal and 60 non-vocal clips extracted from Telugu albums. 11-point moving window is used to ensure the continuity of vocal and non-vocal segments, thus improving the accuracy of system. With this approach system achieves 85.59% accuracy for vocal and 88.52% for non-vocal segment classification. � 2015 IEEE.
Detection of largest possible repeated patterns in Indian audio songs using spectral features
(2016) Thomas, M.; Murthy, Y.V.S.; Koolagudi, S.G.
In the field of Content Based Music Information Retrieval (CB-MIR), researchers are always looking for better ways to classify songs aside from the existing classifiers such as genre, mood, scale, tempo, etc. By determining a way to isolate and extract maximum length repeating patterns (MLRPs) in a music file, we can analyze them in order to describe another potential classifier: complexity. Extraction of repeating patterns would also allow users to easily extract ringtones from their favorite songs. In this paper, an effort has been made to describe a method to extract repeating patterns from a given music file through direct signal level as well as feature level comparison. These extracted patterns can be used as ringtones, or for analysis to determine complexity. Features such as mel-frequency cepstral coefficients (MFCCs), modulation spectral features (MSFs) and jitter are computed to reduce the computational time observed in signal level comparison. � 2016 IEEE.
Food classification from images using convolutional neural networks
(2017) Attokaren, D.J.; Fernandes, I.G.; Sriram, A.; Murthy, Y.V.S.; Koolagudi, S.G.
The process of identifying food items from an image is quite an interesting field with various applications. Since food monitoring plays a leading role in health-related problems, it is becoming more essential in our day-to-day lives. In this paper, an approach has been presented to classify images of food using convolutional neural networks. Unlike the traditional artificial neural networks, convolutional neural networks have the capability of estimating the score function directly from image pixels. A 2D convolution layer has been utilised which creates a convolution kernel that is convolved with the layer input to produce a tensor of outputs. There are multiple such layers, and the outputs are concatenated at parts to form the final tensor of outputs. We also use the Max-Pooling function for the data, and the features extracted from this function are used to train the network. An accuracy of 86.97% for the classes of the FOOD-101 dataset is recognised using the proposed implementation. � 2017 IEEE.
Gesture Recognition using Viola Jones Framework and Contour Detection
(Institute of Electrical and Electronics Engineers Inc., 2017) Karthik, S.; Ramesh, K.; Prakash, S.B.; Murthy, Y.V.S.; Koolagudi, S.G.
The work in this paper suggests a method to recognize gestures of a hand using the approaches of contour detection and Viola Jones framework. At initial stage, hand detection algorithm has been applied to detect the hand using machine learning approach. Later, the methods of contour detection and convex hull are considered for gesture recognition. The system has been trained with variety of gestures and hands with different hand positions. Â© INDIACom-2017.
Objective Assessment of Pitch Accuracy in Equal-Tempered Vocal Music Using Signal Processing Approaches
(2020) Biswas, R.; Murthy, Y.V.S.; Koolagudi, S.G.; Vishnu, S.G.
This paper presents�an approach for assessing the pitch in vocal monophonic music objectively using various�signal processing techniques. A database�has been collected with 250 recordings containing�both arohan and avarohan patterns rendered by 25 different singers for 10 Hindustani classical ragas. The fundamental frequency (F0) values of the user renditions are estimated and analyzed with the original pitch values to quantify the level of variations in pitch initially the five-point moving window has been considered to smoothen the contour. Later, first order and second order differential techniques are applied to estimate the note onset. This process is computationally economical when compared with the available approaches. The technique of cents has been used to evaluate the variation among the target and singing pitch as cent is a unit of the most common tuning system for quantifying intonation in equal tempered music. From this analysis, it is observed that singers with professional training have deviations within 15�20 cents, and non-musicians have deviations above 50 cents. Five expert singers rated the global pitch accuracy from the recordings and these results were found to exhibit high correlation with the system�s assessments. Such an evaluation system with quantitative analysis coupled with visual representation will greatly aid the training process of singers. � 2020, Springer Nature Singapore Pte Ltd.
Performance analysis of LPC and MFCC features in voice conversion using artificial neural networks
(2017) Koolagudi, S.G.; Vishwanath, B.K.; Akshatha, M.; Murthy, Y.V.S.
Voice Conversion is a technique in which source speakers voice is morphed to a target speakers voice by learning source�target relationship from a number of utterances from source and the target. There are many applications which may benefit from this sort of technology for example dubbing movies, TV-shows, TTS systems and so on. In this paper, analysis on the performance of ANN-based Voice Conversion system is done using linear predictive coding (LPC) and mel-frequency cepstral coefficients (MFCCs). Experimental results show that Voice Conversion system based on LPC features is better than the ones based on MFCC features. � Springer Science+Business Media Singapore 2017.
Sound event detection in urban soundscape using two-level classification
(2016) Luitel, B.; Murthy, Y.V.S.; Koolagudi, S.G.
A huge increase in automobile field h as lead t o the creation of different sounds in large volume, especially in urban cities. An analysis of the increased quantity of automobiles will give information related to traffic and vehicles. It also provides a scope to understand the scenario of particular location using sound scape information. In this paper, a two level classification is proposed to classify urban sound events such as bus engine (BE), bus horn (BH), car horn (CH) and whistle (W) sounds. The above sounds are taken as they place a major role in traffic scenario. A real-time data is collected from the live recordings at major locations of the urban city. Prior to the detection of events, the class of events is identified u sing signal processing techniques. Further, features such as Mel-frequency cepstral coefficients (MFCCs) a re extracted based on the analysis of a spectrum of the above-mentioned events and they are prominent to classify even in the complex scenario. Classifiers such as artificial neural networks (ANN), naive-Bayesian (NB), decision tree (J48), random forest (RF) are used at two levels. The proposed approach outperforms the existing approaches that usually does direct feature extraction without signal level analysis. � 2016 IEEE.
The use of Multi-point Crossover Genetic Algorithm for Clustering the Nodes in WSN
(Institute of Electrical and Electronics Engineers Inc., 2018) Batta, K.B.; Rao, G.V.; Murthy, Y.V.S.
In this work, an effort has been done on two important objectives of wireless sensor networks (WSNs) such as optimization of the energy consumption and lifetime. Since these two objectives have lot of impact on producing quality services in wireless networks, these two are considered and the solution is identified through clustering based models. Cluster information is maintained with the as it is appropriate solution for both the objectives. Also, experimentation is done using multi-point crossover genetic algorithm (GA) approach to optimize the time to transmit the information as well as to improvise the life time of a sensor node. Improvised efficiency is found with this effort and the results are compared with existing LEACH and GA using single point crossover techniques. Copy Right Â© INDIACom-2018.
Vocal and Non-vocal Segmentation based on the Analysis of Formant Structure
(2018) Murthy, Y.V.S.; Koolagudi, S.G.; Swaroop, V.G.
The process of classifying vocal and non-vocal regions in an audio clip is the base for many Music Information Retrieval (MIR) tasks. In this work, we have computed novel features based on formant structure for segmenting the vocal and non-vocal regions of a given music clip. The features such as obtuse angles at formant peak, valley locations, convexity, and concavity have been proposed for this task after thorough analysis. The obtuse angles have been computed for second, third and fourth formants as much discrimination is not found for the first formant. The computed formant related features have been added to the base-line Mel frequency cepstral coefficients (MFCCs) in order to improve the performance. Moreover, singer formant (F5) has also been computed forming a 19-dimensional feature vector. As artificial neural networks (ANNs) are more suitable for handling nonlinear data, they have been considered as a classifier. Further, the 11-point moving window has been applied to avoid intermittent misclassifications. An accuracy of 88% is obtained using the proposed approach with a 19-dimensional feature vector. � 2017 IEEE.