Yagya Raj Pandeya, Ph.D.
Citizenship: Nepal
Permanent Address: Jhalari-7, Kanchanpur, Nepal
Mailing Address: Jeonbuk National University, Jeonju city, South Korea
Date of birth: 25th July, 1988
Phone: (+82)01044049848
Carrier Objectives
An enthusiastic and adaptive person with a broad and acute interest in the discovery of new innovative information technologies. I particularly enjoy collaborating with tech exports from different disciplines of computer science to develop new skills and solve new challenges.
Research Intetests
Supervised multi-Level classification and regression
Unsupervised and self-supervised learning
Meta learning and incremental learning
Visual object detection and segmentation
Sound event detection and classification
Educations
Major in Machine Learning and Deep Neural Networks
CGPA: 4.43/4.50
Sep. 2017 - Feb. 2021
Rank: Dean's List (CGPA: 3.97/4)
Aug. 2010 to May 2013
Rank: CGPA: 3.54/4
Aug. 2005 to Oct. 2010
OVERALL PERCENTAGE: 55%
2005
Shree Radha-Krishna Secondary School, Tiltali, Doti(Under Government of Nepal)(Class X)
OVERALL PERCENTAGE: 74%
2003
WORK EXPERIENCES
Machine learning and Deep learning based research
Address: Jeonju City, South Korea
Status: Researcher
27 April 2017 - Ongoing
National Information Collection and Transfer
Address: Singhdurbar, Kathmandu, Nepal
Status: IT officer
27 April 2015 - Aug 22 2016
Assistant Professor and Department head.
Address: Dhangadhi, Kailali, Nepal
Status: Head of Department
12 Feb 2013 - 27 April 2015
Database management.
Address: Dhangadhi, Kailali, Nepal
Status: IT officer (Internship)
21 Aug. 2010 - 10 Sep. 2010
Database management.
Address: Dhangadhi, Kailali, Nepal
Status: IT officer (Internship)
4 Dec. 2009 - 12 Feb. 2010
PROJECTS
Sound Event Labeling Tool
Jeonbuk National University | Advisors: Joonhwan Lee
Dec. 2019 - Sep. 2020
- Article title: A Semi-automatic Sound Annotation Tool for Audio/Video data.[SCIE Journal]
- Semi-supervised Sound Event Annotation Tool using audio and video as input.
- Automatic event detector is used to detect the audio event.
- Based on the automatic detector result, an human annotation have to refine the annotation boundary.
- Easy to use, better audio visualization, python based and output in easy CSV data file.
- Diversified annotation tool for any rare sound event.
- Under review.
Music Source Seperation
Jeonbuk National University | Advisors: Joonhwan Lee
June 2020 - Nov. 2020
- Article title:Parallel Stacked Hourglass Network for Music Source Separation.[SCIE Journal]
- Prepared Korean traditional song (Pansori) dataset with 3 sources.
- Korean traditional music Pansori dataset, MIR-1K dataset, and DSD100 dataset used in experiment.
- Proposed a novel parallel stacked hourglass network (PSHN) with multiple band spectrograms.
- Ablation study on proposed and past architecture.
- State-of-art result.
- Puplished on IEEE Access in Nov. 2020
CNN Based Sound Event Detection in Cowshed
Jeonbuk National University | Advisors: Joonhwan Lee
Dec. 2019 - Sep. 2020
- Article title:Sound Event Detection in Cowshed using Synthetic data and Convolutional Neural Network[IEEE Conference]
- CNN based sound event detection.
- Sound event annotaion tool.
- Sound localization and classification.
- Puplished on ICTC2020 in Sep. 2020
Cow Sound Event Localization and Classification
Jeonbuk National University | Advisors: Joonhwan Lee
Dec. 2019 - Sep. 2020
- Article title:Visual Object Detector for Cow Sound Event Detection[SCIE Journal]
- Cow sound event detection dataset with 4 class categories.
- CNN used for sound event detection using Cow sound dataset and UrbanSound8K dataset.
- Visual object detection architecture (F-RCNN, CF-RCNN, FPN, C-FPC) used for audio event detection (in Log Mel-Spectrogram).
- Compare the proposed CNN and Visual object detection architecture using three test dataset.
- Puplished on IEEE Access in Sep. 2020
Music-Video Emotion Classification
Jeonbuk National University | Advisors: Joonhwan Lee
Jan. 2019 - Sep. 2019
- Article title:Deep Learning-Based Late Fusion of Multimodal Information for Emotion Classification of Music Video[SCIE Journal]
- Music-Video emotion classification using audio and video multimodal network architecture.
- Use pretrained CNN for audio and 3D video model (I3D and C3D).
- The network learned features ate late fused and compare the impact of network feature fusion.
- Cross validation and network feature fusion.
- Puplished on Multimedia Tools and Applications in Sep. 2020
Music Video Emotion Analysis
Jeonbuk National University | Advisors: Joonhwan Lee
Dec. 2018 - March 2019
- Article title:Music-Video Emotion Analysis Using Late Fusion of Multimodal[Conference]
- Music video emotion dataset of six class category.
- Audio-video multimodal architecture.
- C3D pretrained network and CNN pretrained audio network feature fusion.
- Emotion representation in 2D emotion space.
- Puplished on ITEEE 2019 Conference in 2019
Domestic Cat Sound Classification
Jeonbuk National University | Advisors: Joonhwan Lee
Dec. 2017 - Sep. 2018
- Article title:Domestic Cat Sound Classification Using Learned Features from Deep Neural Nets[SCI Journal]
- CNN and CDBN network architecture.
- Cat sound dataset preparation of 10 class categories.
- Frequency division average pooling (FDAP) technique instead of global average pooling (GAP) to make a robust prediction using various frequency band features.
- Audio augmentation and learned feature visualization.
- Puplished on Applied Science in Sep 2018
Domestic Cat Sound Classification using Transfer Learning
Jeonbuk National University | Advisors: Joonhwan Lee
Dec. 2017 - March 2018
- Article title:Domestic Cat Sound Classification Using Transfer Learning[SCIE Journal]
- Cat sound dataset with 10 class categories.
- Use pretrained CNN for feature extraction and make feature classification.
- Machine learning classifier and deep learning classifier comparision.
- Ensemble and data augmentation.
- Puplished on International Journal of Fuzzy Logic and Intelligent Systems in June 2018
Current Research
Achievements
Language Skills
- English Language
- Korean Language
- Hindi
- Nepali
- Good
- Moderate (1 year course)(TOPIK-3)
- Very good
- Excellent
Techincal Skills
- Programming Languages
- Deep learning Framework
- Platforms
- Networking
- I.D.E Skills
- Python, C, C++, PHP
- TensorFlow, Keras, PyTorch
- Linux, Windows, CUDA/Docker
- CCNA
- Eclipse, Spider, UML, PyCharm
References
Ph.D. Adviser
Institude: Jeonbuk National University
Ph No.: +82-63-270-2406, +82-010-9855-2406
Email: chlee@chonbuk.ac.kr
Master Adviser
Institude: Pokhara University
Ph No.: +977-01-5534070
Email: srjoshi@ioe.edu.np
Mr. SS Mudvari
NAST Engineering College Principal
Institude: National Academy of Science & Technology
Ph No.: +977-91-523312,521312
Email varify at: nastdhn@gmail.com, nastdhn@yahoo.com