KP
Krishna Presannakumar
Christ University, Indian Institute of Science Bangalore, Mahatma Gandhi University, Santa Maria Public School and Junior College
Research areas: Computer Science · Signal Processing · Speech Recognition and Synthesis · Music and Audio Processing · Speech and Audio Processing
About
Researcher working with sound signal processing, NLP and deep learning
Employment
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Christ (Deemed to Be University) Assistant Professor2025 - Present
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Indian Institute of Science Bangalore Research Intern2024 - 2025
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Santa Maria Public School and Junior College Post Graduate Tteacher2019 - 2020
Education
- 2020 - 2025
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Mahatma Gandhi University M.Phil2018 - 2019
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Mahatma Gandhi University Master's2015 - 2017
- 2012 - 2015
Projects & Funding
Projects & funding information is unavailable.
Publications (12)
- Enhancing environmental sound classification with weighted attention-based spectrogram fusion and overlapping pre-patching Save
- A Hybrid Non-local Ensemble (HNE) Framework for Robust Image Denoising under Uncertain Noise Conditions Save
- Enhanced Environmental Sound Classification Using Acoustic Feature Aggregation Save
- Improving Flood Prediction Using Artificial Neural Networks With Optimal Feature Selection on a Benchmark Dataset Save
- An enhanced model for environmental sound classification using bio-inspired multi-kernel optimization algorithm Save
- Enhanced Environmental Sound Classification Usingmulti-Feature Fusion and Transformer Pre-Patching Save
- An Enhanced Approach for Environmental Sound Classification Using Multi-window Augmentation Save
- Fusion Spectrogram for Sound Classification Using 2D Convolutional Neural Network Save
- Deep Learning Based Source Identification of Environmental Audio Signals Using Optimized Convolutional Neural Networks Save
- Deep learning based source identification of environmental audio signals using optimized convolutional neural networks Save
- Source identification of weak audio signals using attention based convolutional neural network Save
- An enhanced method for review mining using n-gram approaches Save