Qualification: 
Ph.D, M.Tech, B-Tech

Dr. Jyothish Lal G. currently serves as a Faculty Associate at the Center for Computational Engineering and Networking (CEN), Amrita Vishwa Vidyapeetham, Coimbatore Campus.

He has completed his Bachelor’s Degree in Electronics and Communication Engineering from Cochin University of Science and Technology. He obtained his M.Tech Degree from Amrita Vishwa Vidyapeetham in Computational Engineering and Networking. Prior to Post Graduation, he had three months of experience as Network Field Engineer in HCL Infosystems Ltd and six months on job training in the HCL carrier development center. He also worked as an Assistant Professor at Karpagam Institute of Technology, Coimbatore, for a period of 2 years and 10 months. Later, he joined at the Center for CEN to pursue his Ph.D. degree under the guidance of Dr. E. A. Gopalakrishnan (Ph.D. from IIT Madras).

His research majorly focuses on analyzing time-series data from the human speech production system for characterizing and predicting the dynamics of the system. He has published/communicated four SCI journal papers as a part of his Ph.D. Thesis.

Educational Qualification

  1. 2020: Ph. D. in Engineering,
    Amrita Vishwa Vidyapeetham, 
  2. 2013: M. Tech. in Computational Engineering and Networking,
    Amrita Vishwa Vidyapeetham
  3. 2009: B. Tech. in Electronics and Communication Engineering,
    CUSAT

Publications

Publication Type: Journal Article

Year of Publication Title

2020

Jyothish Lal G., Dr. E. A. Gopalakrishnan, and Dr. Govind D., “Glottal Activity Detection from the Speech Signal Using Multifractal Analysis”, Circuits, Systems, and Signal Processing, vol. 39, no. 4, pp. 2118 - 2150, 2020.[Abstract]


This work proposes a novel method for the detection of glottal activity regions from the speech signal. Glottal activity detection refers to the problem of discriminating voiced and unvoiced segments of the speech signal. This is a fundamental step in the work flow of many speech processing applications. Much of the existing approaches for voiced/unvoiced detection are based on linear measures though the speech is produced from an underlying nonlinear process. The present work solves the problem from a nonlinear perspective, using the framework of multifractal analysis. The fractal property of the speech signal during the production of voiced and unvoiced sounds is sought to obtain the characterization of glottal activity. The characterization is done by computing the Hurst exponent from the evaluation of the scaling property of fluctuations present in the speech signal. Experimental analysis shows that Hurst exponent varies consistently with respect to the dynamics of glottal activity. The performance of the proposed method has been evaluated on the CMU-arctic, Keele and KED-Timit databases with simultaneous electroglottogram signals. Experimental results show that the average detection accuracy or error rate of the proposed method is comparable to the best performing algorithm on clean speech signals. Besides, evaluation of the robustness of the proposed method to noise degradation shows comparable results with other methods for signal-to-noise ratio greater than 10 dB and 20 dB, respectively, for white noise and babble noise.

More »»

2018

Jyothish Lal G., Dr. E. A. Gopalakrishnan, and Dr. Govind D., “Epoch Estimation from Emotional Speech Signals Using Variational Mode Decomposition”, Circuits, Systems, and Signal Processing, vol. 37, pp. 3245–3274, 2018.[Abstract]


This paper presents a novel approach for the estimation of epochs from the emotional speech signal. Epochs are the locations of significant excitation in the vocal tract during the production of voiced sound by the vibration of vocal folds. The estimation of epoch locations is essential for deriving instantaneous pitch contours for accurate emotion analysis. Many well-known algorithms for epoch extraction are found to show degraded performance due to the varying nature of excitation characteristics in the emotional speech signal. The proposed approach exploits the effectiveness of a new adaptive time series decomposition technique called variational mode decomposition (VMD) for the estimation of epochs. The VMD algorithm is applied on the emotional speech signal for decomposition of the signal into various sub-signals. Analysis of these signals shows that the VMD algorithm captures the center frequency close to the fundamental frequency defined for each glottal cycle of emotional speech utterance through its modes. This center frequency characteristic of the corresponding mode signal helps in the accurate estimation of epoch locations from the emotional speech signal. The performance evaluation of the proposed method is carried out on six different emotions taken from the German emotional speech database with simultaneous electroglottographic signals. Experimental results on clean emotive speech signals show that the proposed method provides identification rate and accuracy comparable to that of the best performing algorithm. Besides, the proposed method provides better reliability in epoch estimation from emotive speech signals degraded by the presence of noise.

More »»

2018

Jyothish Lal G., Dr. E. A. Gopalakrishnan, and Dr. Govind D., “Accurate Estimation of Glottal Closure Instants and Glottal Opening Instants from Electroglottographic Signal Using Variational Mode Decomposition”, Circuits, Systems, and Signal Processing, vol. 37, pp. 810–830, 2018.[Abstract]


The objective of the proposed work is to accurately estimate the glottal closure instants (GCIs) and glottal opening instant (GOIs) from electroglottographic (EGG) signals. This work also addresses the issues with existing EGG-based GCI/GOI detection methods. GCIs are the instants at which excitation to the vocal tract is maximum and GOIs, on the other hand, have minimum excitation compared to GCIs. Both these instants occur instantaneously with a fundamental frequency defined for each glottal cycle in a given EGG signal. Accurate detection of these instants from the EGG signal is essential for the performance evaluation of GCIs and GOIs estimated from the speech signal directly. This work proposes a new method for accurate detection of GCIs and GOIs from the EGG signal using variational mode decomposition (VMD) algorithm. The EGG signal has been decomposed into sub-signals using the VMD algorithm. It is shown that VMD captures the center frequency close to the fundamental frequency of the EGG signal through one of its modes. This property of the corresponding mode helps to estimate GCIs and GOIs from the same. Besides, instantaneous pitch frequency is estimated from the obtained GCIs. The proposed method has been evaluated on the CMU-arctic database for GCI/GOI estimation and the Keele pitch extraction reference database for instantaneous pitch frequency estimation. The effectiveness of the proposed method is confirmed by comparison with state-of-the-art methods. Experimental results show that the proposed method has better accuracy and identification rate compared to state-of-the-art methods.

More »»

2012

Jyothish Lal G., Prasannan, N., Das, R., and Dr. Soman K. P., “Software Redefined Communication System”, IOSR Journal of Electronics and Communication Engineering (IOSR-JECE), vol. 4, no. 2, pp. 16-23, 2012.[Abstract]


“Beginning with practical difficulties in teaching communication systems in class room, this paper describes a set of innovative experimental demonstrations developed using SDR”. Communication engineering is one of the interesting, at the same time difficult subject to learn if the concept is not clear or well explained. Normal practice is that the faculties will just give a theoretical class on communication systems and for students the various communication processes like filtering, modulation, demodulation etc. are just imaginary things. Giving a clear idea about these things to a graduate and under graduate student is a little difficult task. The main aim of SDR is to create an attractive learning platform for the students where they are freed from the boring routine of theoretical learning. We are challenging current education system to think outside the “board”, where students will feel excited to learn something new so that they can physically feel and have a real time experience, which would be quite difficult to forget, for which the expense required would be quite high. So in order to better our possibilities, we suggest this software which would equip them to visualize a solid image of what they are learning about. Another fact is that as this software is so simple and is easy to learn and is feasible for everyone who would like to have basics in electronics and communication. This software is worth a stepping stone for those who would dream about being a “real” engineer. This will boost engineering students having great struggle to understand and learn communication subjects. The soul of education system should transform from “cramming as you learn” to “see as you learn’

More »»

Publication Type: Conference Proceedings

Year of Publication Title

2014

Jyothish Lal G. and Veena, V. K., “A Novel Audio Watermark Embedding and Extraction Method Based on Compressive Sensing, Sinusoidal Coding, Reduced SVD, Over Complete Dictionary and L1 Optimization”, Recent Trends in Computer Networks and Distributed Systems Security. Springer Berlin Heidelberg, Berlin, Heidelberg, 2014.[Abstract]


Digital audio watermarking is relatively a new technology to stop audio piracy and to ensure security of the ownership rights of the digital audio data. In this paper, a novel digital watermark embedding and extraction method for audio data is proposed, satisfying the demands of robustness and imperceptibility. This method is based on sinusoidal coding of speech, Compressive Sensing (CS), Reduced Singular Value Decomposition (RSVD), Over-Complete Dictionary (OCD) matrix and L1 optimization algorithm. The sinusoidal approximation of original watermark signal is embedded into the compressive measurements of the host audio signal by using RSVD. Random sampling through compressive sensing ensures compression as well as encryption of the host audio signal. The extraction procedure is based on over-complete dictionary matrix and L1 norm optimization. The over-complete dictionary is created by using sinusoidal speech coding bases and compressive sensing measurement matrix. Experimental results show that proposed method provide exact recovery of watermark information and host signal under noisy attacks.

More »»

2013

Jyothish Lal G., Veena, V. K., and Dr. Soman K. P., “A Combined Crypto-steganographic Approach for Information Hiding in Audio Signals Using Sub-band Coding, Compressive Sensing and Singular Value Decomposition”, Security in Computing and Communications. Springer Berlin Heidelberg, Berlin, Heidelberg, pp. 52-62, 2013.[Abstract]


In this paper, a new method of audio data security system is proposed, which uses the complementary services provided by steganography and cryptography. Here the audio data to be send secretly is encoded using the compressive measurements of the same and the resultant data is embedded in the perceptible band of the cover audio data using the SVD based watermarking algorithm. Thus the combination of these two methods enhances the protection against most serious attacks when audio signals are transmitted over an open channel. Decryption stage uses SVD based watermark extraction algorithm and L1 optimization. Experimental results show that the combined system enhances the security of the audio data embedded.

More »»

2013

Jyothish Lal G., Dr. Soman K. P., and V. K. Veena, “A cryptographic approach to video watermarking based on compressive sensing, arnold transform, sum of absolute deviation and SVD”, Emerging Research Areas and 2013 International Conference on Microelectronics, Communications and Renewable Energy (AICERA/ICMiCR. p. 1,5, 2013.[Abstract]


Video watermarking is relatively a new technology to ensure protection of intellectual property rights and to stop video piracy. The ownership information or watermark is normally hidden in the video sequences. In this paper, a novel video watermarking method is proposed to protect the ownership information in a robust way. This method encrypts the watermark by combining the strengths of Compressive Sensing and Arnold scrambling. The frames for watermark embedding are chosen by computing the Sum of Absolute Deviation between successive frames. The cipher watermark is then embedded into chosen frames based on SVD watermark embedding algorithm. The decryption stage performs SVD watermark extraction algorithm, Arnold inverse transform and L 1 optimization for retrieval of watermark. Experimental results show that proposed method enhances the security of the ownership information embedded.

More »»

2012

V. K. Veena, Jyothish Lal G., S. Vishnu Prabhu, S. Sachin Kumar, and Dr. Soman K. P., “A robust watermarking method based on Compressed Sensing and Arnold scrambling”, 2012 International Conference on Machine Vision and Image Processing (MVIP). Publisher: IEEE, Taipei, Taiwan, 2012.[Abstract]


Watermarking is a technique for information hiding, which is used to identify the authentication and copyright protection. In this paper, a new method of watermarking scheme is proposed, which uses both Compressed Sensing and Arnold scrambling method for efficient data compression and encryption. Compressive sensing technique aims at the reconstruction of sparse signal using a small number of linear measurements. Compressed measurements are then encrypted using Arnold transform. The proposed encryption scheme is computationally more secure against investigated attacks on digital multimedia signals.

More »»