Dual Tone Multi-Frequency (DTMF) Decoder

Overview

As the project leader for CS 1330 - Computational Thinking Data Science, I led a team to develop a Dual Tone Multi-Frequency (DTMF) Decoder in Python. This innovative project combines data science and machine learning libraries with Python scripting to decode telephone keypad digits from WAV audio files. By integrating Fast Fourier Transform (FFT), we not only analyzed audio signals but also matched them with their corresponding numeric digits, showcasing our ability to efficiently decode DTMF signals and highlighting our team's technical proficiency.

Features

• DTMF Encoding & Decoding: Generates frequencies for dial tones and decodes them back to digits.
• Audio File Processing: Analyzes WAV audio files to slice each tone and decode digits.
• FFT Implementation: Utilizes FFT to match audio frequencies with predefined number database.

Technologies Used

• Python for scripting and automation.
• Libraries: NumPy for FFT, pandas for data manipulation, and SciPy for signal processing.

Getting Started

To run this project:

1. Clone the repository.
2. Ensure you have Python and necessary libraries installed.
3. Run the Jupyter Notebooks (DTMF - Part 1 - Final.ipynb through DTMF - Part 4 - Final.ipynb) in sequence to see the encoding, decoding, and analysis process.

How It Works

The decoder takes an audio file, slices each tone, and uses FFT to compare frequencies against a database, identifying the corresponding digit for each tone sequence.