Difference between revisions of "BeagleBoard/GSoC/2020Proposal/AnirudhSivakumar"

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[[Category: GSoCProposal2020]]
 
[[Category: GSoCProposal2020]]
  
=[[https://elinux.org/BeagleBoard/GSoC/2020Proposal/AnirudhSivakumar Proposal for Analysing school students emotions and problems using deep learning algorithm with BeagleBone Black]] =
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=[[https://elinux.org/BeagleBoard/GSoC/2020Proposal/AnirudhSivakumar Proposal for Emotions Recognition in School Kids using Deep Learning and NLP with BeagleBone Black]] =
 
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''Code'': https://github.com/Aneroid666/gsoc-application/tree/new_branch<br>
 
''Code'': https://github.com/Aneroid666/gsoc-application/tree/new_branch<br>
 
''Wiki'': https://elinux.org/BeagleBoard/GSoC/2020Proposal/AnirudhSivakumar<br>
 
''Wiki'': https://elinux.org/BeagleBoard/GSoC/2020Proposal/AnirudhSivakumar<br>
''GSoC'': [N/A]<br>
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''GSoC'': <br>
 
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<div style="clear:both;"></div>
  
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=Proposal=
 
=Proposal=
Completed all the requirements listed on the [http://bbb.io/gsocideas ideas page]. The code for the task can be found in the github repository [https://github.com/Aneroid666/gsoc-application/tree/new_branch here] submitted through the pull request [https://github.com/jadonk/gsoc-application/pull/143 #143] generated in github.
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Completed all the requirements listed on the [http://bbb.io/gsocideas ideas page]. The code for the task can be found in the Github repository [https://github.com/Aneroid666/gsoc-application/tree/new_branch here] submitted through the pull request [https://github.com/jadonk/gsoc-application/pull/143 #143] generated in Github.
  
 
==About you==
 
==About you==
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==About your project==
 
==About your project==
''Project name'': Analysing school students emotions and problems using deep learning algorithm with BeagleBone Black<br>
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''Project name'': Emotions Recognition in School Kids using Deep Learning and NLP with BeagleBone Black<br>
  
 
===Description===
 
===Description===
The idea of the project is to make a system that can identify a person's emotions and problems by using audio signal processing on the way they talk and present themselves when interviewed. This system is planned to be used on school students and college students. Mental health is something that is upcoming these days and students suffer from depression and try and hide their feelings from people because they are too scared to open up. This system helps to identify those people who try and hide their emotions and can thus be helped in many ways.
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School kids are often unrecognised in many of the Asian educational system. They carry various emotions, right from parent’s fights to getting bullied. Kids often make intangible opinions about their life and future lifestyle based on experienced perception, thus impacting their carrier. The education system of many countries advice to identify such students and make a personal peer to peer counselling system. Apart from human intervention in decision making, we are looking forward to creating a trained algorithm which can identify such induced emotions and identify students who aren’t approachable nor identified by teachers.
The project will use aa BeagleBone Black and a microphone circuit to take audio input from the subject and using Python coding language, a deep learning algorithm will be created accordingly using signal template mapping, which can predict the emotions. The system will be tested on students from schools nearby and can thus be perfected.
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Natural Language Processing (NLP) using deep learning algorithm has proven to be the best fit for decision making over human intelligence in speech recognition. A deep learning algorithm is always built and trained over time to make it best fit for small range applications like a mobile app or a desktop executable file.  
The data can also be uploaded to a cloud, where appropriate processing can be done and the data can be accessed from anywhere.
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The project deals in three stages:
 
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The project deals in three stages. The first stage includes the use of beaglebone black and a microphone, which is capable enough to record interview voices of students from various schools in India, and store the data via the cloud. The second stage consists of storing data to be fed to a deep learning algorithm and keep training until it identifies as a best-fit percentage and the third stage deals with the design and development of a mobile application to be remotely used and to test the efficiency of the trained algorithm.  
 +
The idea behind the project is to identify the emotions of the school kids and their social behaviour. This helps the teachers to make more efficient peer to peer counselling. The outcome of the project would be long term research over speech recognition using NPL and Deep learning and help scientific society with best-fit speech samples for future R&D in economically slow countries.
  
 
===Timeline===
 
===Timeline===

Revision as of 08:06, 30 March 2020


[Proposal for Emotions Recognition in School Kids using Deep Learning and NLP with BeagleBone Black]

A short summary of the idea will go here.

Student: Anirudh Sivakumar
Mentors:
Code: https://github.com/Aneroid666/gsoc-application/tree/new_branch
Wiki: https://elinux.org/BeagleBoard/GSoC/2020Proposal/AnirudhSivakumar
GSoC:

Status

This project is currently just a proposal.

Proposal

Completed all the requirements listed on the ideas page. The code for the task can be found in the Github repository here submitted through the pull request #143 generated in Github.

About you

IRC: Anirudh666
Github: Anirudh666
School: Manipal Institute of Technology, Manipal
Country: India
Primary language : English, Tamil
Typical work hours : 7AM to 9PM IST

About your project

Project name: Emotions Recognition in School Kids using Deep Learning and NLP with BeagleBone Black

Description

School kids are often unrecognised in many of the Asian educational system. They carry various emotions, right from parent’s fights to getting bullied. Kids often make intangible opinions about their life and future lifestyle based on experienced perception, thus impacting their carrier. The education system of many countries advice to identify such students and make a personal peer to peer counselling system. Apart from human intervention in decision making, we are looking forward to creating a trained algorithm which can identify such induced emotions and identify students who aren’t approachable nor identified by teachers. Natural Language Processing (NLP) using deep learning algorithm has proven to be the best fit for decision making over human intelligence in speech recognition. A deep learning algorithm is always built and trained over time to make it best fit for small range applications like a mobile app or a desktop executable file. The project deals in three stages: The project deals in three stages. The first stage includes the use of beaglebone black and a microphone, which is capable enough to record interview voices of students from various schools in India, and store the data via the cloud. The second stage consists of storing data to be fed to a deep learning algorithm and keep training until it identifies as a best-fit percentage and the third stage deals with the design and development of a mobile application to be remotely used and to test the efficiency of the trained algorithm. The idea behind the project is to identify the emotions of the school kids and their social behaviour. This helps the teachers to make more efficient peer to peer counselling. The outcome of the project would be long term research over speech recognition using NPL and Deep learning and help scientific society with best-fit speech samples for future R&D in economically slow countries.

Timeline

Provide a development timeline with a milestone each of the 11 weeks and any pre-work. (A realistic timeline is critical to our selection process.)

Mar 30 Proposal complete, Submitted to https://summerofcode.withgoogle.com
Apr 27 Proposal accepted or rejected
May 18 Pre-work complete, Coding officially begins!
May 25 Milestone #1, Introductory YouTube video
June 1 Milestone #2
June 8 Milestone #3
June 15 18:00 UTC Milestone #4, Mentors and students can begin submitting Phase 1 evaluations
June 19 18:00 UTC Phase 1 Evaluation deadline
June 22 Milestone #5
June 29 Milestone #6
July 6 Milestone #7
July 13 18:00 UTC Milestone #8, Mentors and students can begin submitting Phase 2 evaluations
July 17 18:00 UTC Phase 2 Evaluation deadline
July 20 Milestone #9
July 27 Milestone #10
August 3 Milestone #11, Completion YouTube video
August 10 - 17 18:00 UTC Final week: Students submit their final work product and their final mentor evaluation
August 17 - 24 18:00 UTC Mentors submit final student evaluations

Experience and approach

I have been part of 2 student project teams. I was in the electronics and control subsystem in Formula Manipal and I am currently the co-founder and electronics and propulsion head in loopMIT. I am also currently in the semi-finals of IICDC 2019 with the project E_agri, which is a smart agricultural system based on IoT. I have a lot of experience in working with electronics and embedded systems. I have also designed a Data Acquisition system using the BeagleBone Black for the Formula Manipal Electric car, which communicated through CAN protocol and acted as the master controller of the system. I am also set to present a research paper at the international conference of ACTSE 2020 and I am currently working on another research paper based on deep learning. I also did an internship at Center for Artificial Intelligence and Robotics (Defence Research Development Organisation, India) where I worked on the development of cost map for a hex-copter using ROS on Jetson TX1 Since I have worked with BeagleBone Black before, I will start with designing the hardware part of the circuit, and make a dedicated PCB for it. Then I will start with the coding for the deep learning algorithm, using Python.

Contingency

If I was to get stuck on a problem, I believe that I will first start looking into all the systems that are linked to that problem and I will read about those systems in detail and see if I can find other solutions online, so I can get back to the system with a better approach. Since there are a plethora of resources online, about the BeagleBone controllers, Neural Networks and Deep learning, I will be able to find a solution to the problem. If, after multiple attempts, if I am still not able to correct the system, I will approach one of my faculties at my university, who has worked in this field of embedded systems and deep learning.

Benefit

If completed, it will help in understanding the emotions of not only students, but all individuals in all fields who are facing problems regarding mental health, and it can help them seek attention. Include quotes from BeagleBoard.org community members who can be found on http://beagleboard.org/discuss and http://bbb.io/gsocchat.

Misc

Please complete the requirements listed on the ideas page. Provide link to pull request.

Suggestions

Is there anything else we should have asked you?