SpectrumCraft: Fourier Analysis for Audio Signal Processing

Overview

This repository showcases my transition from "pure mathematics" in my B.Sc. (Mathematics) project to a "practical computer science application" during my MCA.

The original UG project explored "Fourier Analysis" — a mathematical method to decompose complex signals into simple sine and cosine waves.
I extended this into a "Python-based Audio Signal Processing tool" using the "Fast Fourier Transform (FFT)" for real-world tasks such as noise reduction, speech spectrogram generation, and music frequency analysis.

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Features

• "UG Mathematics Project" — Original theoretical research on Fourier Analysis and applications.
• "CS Implementation" — Python FFT code for audio processing.
• "Audio Frequency Analysis" — View frequency spectrum of .wav files.
• "Noise Reduction" — Remove unwanted frequencies above a chosen cutoff.
• "Spectrogram Visualization" — Before & after comparison.

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How It Works

1. "Load Audio" → Read .wav file.
2. "FFT Analysis" → Convert waveform from time to frequency domain.
3. "Filter Noise" → Remove unwanted frequencies.
4. "Inverse FFT" → Rebuild clean audio signal.
5. "Save & Visualize" → Output .wav file and spectrogram images.

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Setup & Run Locally

1. Clone the repository

git clone https://github.com/YOUR_USERNAME/SpectrumCraft-Fourier-Analysis-for-Audio-Signal-Processing.git cd SpectrumCraft-Fourier-Analysis-for-Audio-Signal-Processing

Install Dependencies pip install -r requirements.txt

Run this scripts python CS_Extension_Code/audio_fft_noise_reduction.py