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+# Audio Classification
+
+## Project Description
+
+This project was created for the Independent Study and Mentorship program. The ISM program allows Frisco ISD students 
+to explore interested fields through research and mentorship, developing life-long skills in the process. Students 
+showcase their knowledge through the semester-long projects known as the Original Work and Final Product. Furthermore, students acquire mentorship from
+professionals, developing communication and valuable connections.
+
+This year in ISM, I am exploring the field of Machine Learning with a special focus on audio classification. This 
+repository documents my code for my Original Work and provides an easy-to-use UI for navigating through my code.
+
+## Quick Links
+
+* [Commits](https://github.com/AnanthVivekanand/audio-classification/commits): Documentation of my work over time in the form of incremental updates.
+* [Digital Portfolio](http://ananthvivekanand.weebly.com): My digital portfolio showcasing my work in ISM.
+
+## Getting started
+
+This guide assumes you have basic familiarity with the terminal. 
+
+1. Open a terminal environment. 
+1. Clone this repository: `git clone https://github.com/AnanthVivekanand/audio-classification.git`
+2. Navigate into the `GUI/` directory: `cd audio-classification/GUI/`
+3. Install all python dependencies: `pip3 install -r requirements.txt`
+4. Then run `application.py`: `python3 ./application.py`
+5. Navigate to `http://localhost:5000/` in your web browser
+
+Documentation:
+
+* [`GUI/application.py`](GUI/application.py): This is a simple [Flask application](https://flask.palletsprojects.com/en/1.1.x/) that creates a HTTP API for the audio classification model. 
+* [`GUI/templates/home.html`](GUI/templates/home.html): This is the front-end HTML code that the provides a file upload and visualization interface.
+* [`final_model/weights.h5`](final_model/weights.h5): This binary file contains the "weights" of the trained machine learning model. 
+* [`preprocessing/extract_fft_data.ipynb`](preprocessing/extract_fft_data.ipynb): This file extracts Short-time Fourier Transform data from .wav files in the dataset. This allows for faster training as the STFT is computed only once and stored indefinetely.
+* [`training/optimized_TCN.ipynb`](training/optimized_TCN.ipynb): This file implements a Temporal Convolutional Neural Network that classifies STFT inputs into one of 10 catagories. This is the model that the Flask application uses.
+
+Acknowledgements:
+
+* Dr. Abhishek Sehgal at Samsung Research America
+* Mr. Gautum Bhat at UTD's Statistical Signal Processing Research Laboratory