SpectraForge

DSP & Communication Signal Analysis Lab

SpectraForge is a research-oriented Digital Signal Processing (DSP) and Communication Systems project that demonstrates signal generation, noise modeling, frequency-domain analysis, filtering, and communication performance evaluation.

This repository contains Python, Jupyter Notebook, and MATLAB implementations of the same core experiments, allowing cross-verification and flexible demonstration.

For detailed experiment explanations, observations, and extended results,
please refer to:
SpectraForge/SpectraForge/README.md

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Project Highlights

• End-to-end DSP and communication system simulation
• Automated, reproducible experiment pipeline
• Time-domain and frequency-domain signal analysis
• Digital filtering for noise reduction
• BER vs SNR evaluation for BPSK over AWGN
• Implemented in Python, Jupyter Notebook, and MATLAB

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Visual Results Overview

The following sections highlight representative outputs generated automatically by the project. All images shown here are produced directly by the code.

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Time-Domain Signal Analysis

Comparison of clean and noisy signals in the time domain.

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Frequency-Domain Analysis (FFT)

Frequency spectrum of clean and noisy signals obtained using FFT.

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Digital Filtering Results

Effect of low-pass filtering on noisy signals.

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Communication Performance (BER vs SNR)

Bit Error Rate performance of a BPSK system over an AWGN channel.

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MATLAB Pipeline Results

The same DSP and communication experiments are implemented in MATLAB to validate results using an academic-style signal processing environment.

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Jupyter Notebook Demonstration

An interactive Jupyter Notebook is provided for step-by-step execution, visualization, and explanation of the entire experiment pipeline.

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Repository Structure (High-Level)

SpectraForge/
├── SpectraForge.ipynb
├── python/
├── matlab/
├── outputs/
├── report/
└── requirements.txt

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How to Explore the Project

Python (Automated Pipeline)

cd python
python pipeline.py

Jupyter Notebook

Open SpectraForge.ipynb and run all cells sequentially.

MATLAB

Upload the matlab/ folder to MATLAB Online or MATLAB Desktop and run:

run_pipeline

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Notes for Reviewers

• All plots shown are generated programmatically
• No manual screenshots are required
• Results are reproducible by re-running the pipeline
• Python and MATLAB outputs follow the same methodology

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Additional Documentation

For in-depth explanations, design decisions refer to the internal documentation:

SpectraForge/SpectraForge/README.md