Sangeetha Rajesh

Associate Professor – Research & Business Analytics

about faculty

Education qualification- Ph. D. in Computer Applications, Annamalai University
Thesis: Music Instrument analysis and singer identification using machine
learning techniques
Post Graduate Program in Data Science and Business Analytics Great
Learning and University of Texas at Austin, McCombs School of Business
Master of Computer Applications, Indira Gandhi National Open
University
BSc. Computer Science, School of Technology and Applied Sciences,
Mahatma Gandhi University

Teaching Experience- 17 years

Industry Exposure-

Academic Affiliation-
Reviewer:
• International Journal of Fuzzy System
• American Journal of Applied Sciences
• American Journal of Engineering and Applied Sciences
• Journal of Computer Science
• International Journal of Reconfigurable and Embedded Systems

Professional Affiliation –
• ISTE - Member

teaching & research

Teaching Interest: Artificial Intelligence, Machine Learning, Deep Learning, NLP, Business Intelligence, SQL & NOSQL Databases, Spreadsheet Modeling, Python & R for data science, Information Systems

Current research: AI & Blockchain in Supply Chain Management

consulting

publications

Publication Released

Sangeetha Rajesh & N. J. Nalini (2024) Instrument Emotion Recognition from Polyphonic Instrumental Music using MFCC and CENS Features with Deep Neural Networks, Procedia Computer Science 235, 2548-2556. [Scopus]
Sangeetha Rajesh & N. J. Nalini (2023) “Polyphonic Instrument Emotion Recognition using Stacked Auto Encoders: A Dimensionality Reduction Approach, Procedia Computer Science, Elsevier, Volume 218, pp.1905-1914. [Scopus]
Sangeetha Rajesh. & N. J. Nalini. (2021), Recognition of Musical Instrument Using Deep Learning Techniques, International Journal of Information Retrieval Research, Vol. 11(4). [WoS]
Sangeetha Rajesh. & N. J. Nalini. (2021), Combined evidence of MFCC and CRP features using machine learning techniques for singer identification, International Journal of Pattern Recognition and Artificial Intelligence, Vol. 35(1), 2158001. [Scopus & WoS]
Sangeetha Rajesh. & N. J. Nalini. (2019), Singer Identification using MFCC and CRP with Support Vector Machines, Springer Advances in Intelligent Systems and Computing, Computational intelligence in pattern recognition, Vol 999, pp.295-306. [Scopus]
Sangeetha Rajesh, Nilakantan Narasinganallur, Trushal Asolkar, (2019) Box office Prediction using Predictive Analytics, Indian Journal of Economics and Businesses. [Scopus & ABDC].

Paper presented

Instrument Emotion Recognition from Polyphonic Instrumental Music using MFCC and CENS Features with Deep Neural Networks, International Conference on Information Technology: IoT and Smart City,2024, University of Malaya, Kuala Lumpur, Malaysia.
Instrument emotion recognition from polyphonic instrumental music using MFCC and CENS features with Dee Neural Networks, International Conference on Machine Learning and Data Engineering, ICMLDE 2023, UPES, Dehradun
Predicting Stack Overflow Tag: A Deep Learning Approach, IEEE 9th Somaiya International Conference on Technology and Information Management, 2023, K J Somaiya Institute of Management, Somaiya Vidyavihar University, Mumbai.
Polyphonic Instrument Emotion Recognition using Stacked Auto Encoders: A Dimensionality Reduction Approach, International Conference on Machine Learning and Data Engineering, ICMLDE 2022, UPES, Dehradun.

Singer Identification using MFCC and CRP with Support Vector Machines in the international conference on Computational Intelligence and Pattern Recognition, CIPR 2019, Indian Institute of Engineering Science and Technology, Kolkata.

Sangeetha Rajesh

about faculty

teaching & research

consulting

publications

awards & honors