Back close
Admissions

Dr. Lekshmi C. R.

Assistant Professor, Centre for Computational Engineering and Networking, School of Artificial Intelligence, Coimbatore

Qualification: M.Tech., Ph.D
cr_lekshmi@cb.amrita.edu
Orcid Profile
Research Interest: Signal Processing using deep learning, Neural networks, Generative adversarial networks, Transformer architectures, Attention mechanism

Bio Publications Qualifications Positions Awards/Honors/Recognition Miscellaneous
Bio

Dr. Lekshmi C. R. is an Assistant Professor at the Centre for Computational Engineering and Networking, School of Artificial Intelligence, Amrita Vishwa Vidyapeetham, Coimbatore. She earned her Ph.D. from the College of Engineering Trivandrum, focusing on “Predominant Instrument Recognition and Separation in Polyphonic Music Using Deep Learning Frameworks.”

Her research interests include image and audio processing with deep learning, attention mechanisms, generative adversarial networks, and transformer architectures. Dr. Lekshmi holds a B.Tech in Electronics and Communication Engineering from CUSAT and a Master’s in Applied Electronics from Anna University, Chennai.

Publications

Conference Paper

Year : 2025

Ai-Powered Pencil Strokes: Face Sketch Synthesis Using Cyclegan and Style Transfer

Cite this Research Publication : Sighakolli Dheeraj Venkata Sai, Simma Sathwik, Solleti Venkata Dhiraj, Peddi Deekshith, C. R Lekshmi, Ai-Powered Pencil Strokes: Face Sketch Synthesis Using Cyclegan and Style Transfer, 2025 International Conference on Inventive Computation Technologies (ICICT), IEEE, 2025, https://doi.org/10.1109/icict64420.2025.11005149

Publisher : IEEE

Year : 2025

Robust CNN-based Musical Instrument Recognition with Enhanced Feature Learning

Cite this Research Publication : Padmesh Sivalingam, Aamith Kishore T J, Sri Krishna P, Yaswanth Reddy B, Ragav S, Lekshmi C. R., Robust CNN-based Musical Instrument Recognition with Enhanced Feature Learning, 2025 International Conference on Inventive Computation Technologies (ICICT), IEEE, 2025, https://doi.org/10.1109/icict64420.2025.11005342

Publisher : IEEE

Year : 2024

Compact Convolutional Transformers for Multiple Predominant Instrument Recognition in Polyphonic Music

Cite this Research Publication : Lekshmi C. R., Rajeev Rajan, Compact Convolutional Transformers for Multiple Predominant Instrument Recognition in Polyphonic Music, 2024 9th International Conference on Communication and Electronics Systems (ICCES), IEEE, 2024, https://doi.org/10.1109/icces63552.2024.10860152

Publisher : IEEE

Year : 2021

Predominant Instrument Recognition in Polyphonic Music Using Convolutional Recurrent Neural Networks

Cite this Research Publication : Lekshmi.C.R. and Rajan, R., “Predominant Instruments recognition in Polyphonic music using Convolutional Recurrent Neural Networks”, in Proc. of the 15th International Symposium on CMMR, Tokyo, Japan,Nov. 15-19, 2021 ,pp 185-194

Year : 2021

Attention-based Predominant Instruments Recognition in Polyphonic Music

Cite this Research Publication : Lekshmi C. Reghunath. and Rajeev Rajan., “Attention-based Predominant Instruments Recognition in Polyphonic Music”, in Proceedings of 18th Sound and Music Computing Conference (SMC), Torino, Italy, 29 June – 01 July 2021, pp. 199–206

Journal Article

Year : 2025

Dynamic Feature Learning with Involution and Convolution for Predominant Instrument Recognition in Polyphonic Music

Cite this Research Publication : C. R. Lekshmi, Jishnu Teja Dandamudi, Dynamic Feature Learning with Involution and Convolution for Predominant Instrument Recognition in Polyphonic Music, Circuits, Systems, and Signal Processing, Springer Science and Business Media LLC, 2025, https://doi.org/10.1007/s00034-025-03111-y

Publisher : Springer Science and Business Media LLC

Year : 2025

Beyond transformers: hierarchical contextualization and gated aggregation for multiple predominant instrument recognition in polyphonic music

Cite this Research Publication : C. R. Lekshmi, Rajeev Rajan, Beyond transformers: hierarchical contextualization and gated aggregation for multiple predominant instrument recognition in polyphonic music, The Journal of Supercomputing, Springer Science and Business Media LLC, 2025, https://doi.org/10.1007/s11227-025-07279-7

Publisher : Springer Science and Business Media LLC

Year : 2023

Predominant audio source separation in polyphonic music

Cite this Research Publication : Reghunath,L.C.,Rajan,R., ”Predominant Audio Source Separation”, Eurasip Journal of Audio Speech and Music Processing 49, (2023), , Impact factor: 2.4, SJR: 0.458, Indexing: SCIE

Year : 2023

Multiple Predominant Instruments Recognition in Polyphonic Music using Spectro/Modgd-gram Fusion

Cite this Research Publication : Lekshmi C. R., Rajeev, R. “Multiple Predominant Instruments Recognition in Polyphonic Music using Spectro/Modgd-gram Fusion”, Circuits Syst Signal Process (CSSP) ,42, 3464–3484 (2023)., ,Impact factor: 2.3, SJR: 0.494, Indexing: SCIE

Year : 2022

POMET: a corpus for poetic meter classification

Cite this Research Publication : Rajan, R., ChandrikaReghunath, L. & Varghese, L.T. POMET: a corpus for poetic meter classification. Lang Resources & Evaluation 56, 1131–1152 (2022)., Impact factor: 2.7, Indexing: SCIE

Year : 2022

Transformer-based Ensemble Method for Multiple Predominant Instruments Recognition in Polyphonic Music

Cite this Research Publication : Reghunath, L C., Rajan, R., “Transformer-based Ensemble Method for Multiple Predominant Instruments Recognition in Polyphonic Music”, Eurasip Journal of Audio Speech and Music Processing (2022), Impact factor: 2.4, SJR: 0.458, Indexing: SCIE

Book Chapter

Year : 2021

Predominant Instruments recognition in Polyphonic music using Convolutional Recurrent Neural Networks

Cite this Research Publication : Predominant Instruments recognition in Polyphonic music using Convolutional Recurrent Neural Networks”, in Proceedings of 15th International Symposium on Computer Music Multidisciplinary Research (CMMR-21), Tokyo, Japan, Nov 15 - 19, 2021, pp 185-194. In: Revised selected papers ,Music in the AI Era,. Lecture Notes in Computer Science, vol13770 . Springer, Cham

Qualifications
  • PhD : 2019-2023
    Specialization: Signal processing using deep learning
    Thesis Title: Predominant Instrument Recognition and Separation in Polyphonic Music Using Deep Learning Frameworks.
  • M.Tech : 2013
    Specialization: Applied Electronics
    Thesis Title: Color document image authentication with data repair capability.
Positions
  • Assistant Professor from 03/06/2024 to till date at Amrita Viswa Vidyapeetham, Coimbatore campus
  • Worked as Assistant Professor in various engineering colleges.(Total experience: 7 years)
Awards/Honors/Recognition
  • 3rd rank in KTU Ph. D. entrance (Availed KTU CERD Fellowship-2019), 31 st rank in ME Applied electronics (2013)
Miscellaneous

Membership/Associations

  • International SpeechCommunication Association (ISCA)-Member-2025-26

Reviewers (National/International Journals; Conferences)

  • IEEE Transactions on Audio, Speech, and Language Processing (TASLP)-ISSN: 1558-7916
  • Scientific Reports, Springer-ISSN: 2045-2322
  • IEEE International Conference on Acoustics Speech and Signal Processing (ICASSP)
  • Journal of Supercomputing, Springer-ISSN: 1573-0484
  • ASEAN Journal on Science and Technology for Development, Elsevier-ISSN: 2224-9028

>

Admissions Apply Now