Seasons Of Code
Blind Source Separation • Riddhish Bhalodia
WnCC - Seasons of Code
Seasons of Code is a programme launched by WnCC along the lines of the Google Summer of Code. It provides one with an opprtunity to learn and participate in a variety of interesting projects under the mentorship of the very best in our institute.
List of Running Projects
- Browser Based PDF manager
- Resume Script Generator
- Physicc : A Simple Physics Engine
- Image Colorization
- Language Model Based Syntax Autocompletion in a Text Editor
- Computer vision based web app
- Cribbit Cribbit (Open for PGs Only)
- Techster Texter
- Language Detection
- Book Tracker
- ResoBin - Not the bin we deserve but the bin we need!
- Moodify
- Agree to disagree
- Unscripted
- Watson (World's smartest assistant in your pocket)
- IITinder
- BriefKing
- Meta Learning - Learning to Learn
- Break free of the matrix, by building one!
- Procedurally Generated Infinite Open World
- Introduction to App Development
- PAC MAN
- Introduction to Web Development
- (Un)Clear
- Goal ICPC
- Traffic congestion modelling and rendering
- PyRated
- Tools for Data Science
- Machine Learning Based Metropolitan Air Pollution Estimation
- Audio controlled drone
- NLPlay with Transformers
- DIY FaceApp
- A Deep Dive into CNNs
- Competitive Coding
- Snake AI
- Facial Recognition App
- Gaming meets AI !!!
- R(ea)L Trader
- Computational Geometry
- Deep reinforcement learning - 2048 AI
- Reinforcement Learning to Finance
- Developing Hybrid ANN-Statistical Model for Robust Stock Market Prediction
- Si-Phy
- Astronomical Data-modelling and Interpretation
- Visual Perception for Self Driving Cars
- Convolutional Neural Networks and Applications
- Quantum Computing Algorithms
- Algorithm Visualizer
- Anime Club IITB Website using Django
- Machine Learning in Browser
Blind Source Separation
The project involves exploring various implementations of Independent Component Analysis on sound/images and demonstrating through an ipython notebook.
We explored two different approaches to the problem of separating sources of audio signals by minimizing the statistical dependence - fastICA and FOBI. A detailed explanation of the theory and the code can be found on the rendered notebook here. You can download and use the code and dataset from the repository. The notebook can also be found on the official iPython Wiki.