SUGAN NAGARAJAN

About

Sugan Nagarajan received the B.E. degree in Electronics and Communication Engineering from Alagappa Chettiar Government College of Engineering and Technology, Karaikudi, Tamil Nadu, India, in May 2014, and the M.E. degree in Applied Electronics from Anna University, Chennai, Tamil Nadu, India, in April 2016. He completed his Ph.D. degree in the Department of Electronics and Communication Engineering at National Institute of Technology Puducherry, Karaikal, India, in June 2026.

From September 2016 to October 2016, he served as a Project Assistant, and from November 2016 to July 2019, he worked as a Lab Engineer under the SMDP-C2SD Project funded by the Ministry of Electronics and Information Technology (MeitY), Government of India, in the Department of Electronics and Communication Engineering at National Institute of Technology Puducherry, Karaikal, India. Since July 2019, he has been serving as an Assistant Professor in the Department of Electronics and Communication Engineering at Government College of Engineering Bodinayakkanur.

His research interests include Digital Signal Processing, Speech Recognition, Speech Emotion Recognition, FPGA-based Digital VLSI System Design, Analog VLSI, Machine Learning, and the Internet of Things (IoT). He has authored and co-authored several research publications in these areas.

Employment

• Government College of Engineering Bodinayakkanur Assistant Professor
  2019 - Present
• National Institute of Technology Puducherry Lab Engineer
  2016 - 2019
• National Institute of Technology Puducherry Project Assistant
  2016 - 2016

Education

• National Institute of Technology Puducherry Ph.D.
  2018 - 2026
• Anna University Chennai Master of Engineering
  2014 - 2016
• Alagappa Chettiar College of Engineering and Technology Bachelor of Engineering
  2010 - 2014

Projects & Funding

Projects & funding information is unavailable.

Publications (9)

• Fusion of auditory frequency scale features and deep spectral features for the detection of Parkinson’s disease using various classifiers
  Biomedical Signal Processing and Control 2026 DOI: 10.1016/j.bspc.2026.111235
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• Speech Emotion Recognition Using Bark-Scale Cepstral Features and MLP Classifier on RAVDESS Speech Corpus
  2025 IEEE 22nd India Council International Conference (INDICON) 2025 DOI: 10.1109/indicon68490.2025.11392890
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• Speech Emotion Recognition Using Cepstral Features Extracted With Gammatone Filter Banks Realized Based on ERB and Mel Frequency Scales
  IEEE Access 2025 DOI: 10.1109/ACCESS.2025.3644961
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• FPGA-Based Design of a Ready-to-Use and Configurable Soft IP Core for Frame Blocking Time-Sampled Digital Speech Signals
  Electronics 2024 DOI: 10.3390/electronics13214180
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• Speech emotion recognition using cepstral features extracted with novel triangular filter banks based on bark and ERB frequency scales
  Digital Signal Processing 2020 DOI: 10.1016/j.dsp.2020.102763
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• Digital Architecture for Instantaneous V/UV/S Classification of Noise Free Speech Segments
  2020 24th International Symposium on VLSI Design and Test (VDAT) 2020 DOI: 10.1109/vdat50263.2020.9190224
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• Recognition of Spoken Languages from Acoustic Speech Signals Using Fourier Parameters
  Circuits, Systems, and Signal Processing 2019 DOI: 10.1007/s00034-019-01100-6
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• Performance Comparison of Different Cepstral Features for Speech Emotion Recognition
  2018 International CET Conference on Control, Communication, and Computing (IC4) 2018 DOI: 10.1109/CETIC4.2018.8531065
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• Speaker-Independent Japanese Isolated Speech Word Recognition Using TDRC Features
  2018 International CET Conference on Control, Communication, and Computing (IC4) 2018 DOI: 10.1109/CETIC4.2018.8530947
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