WaveDecode

A Python-based signal analysis platform for capturing, processing, visualizing, and classifying radio frequency (RF) transmissions using Software Defined Radio (SDR).

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Overview

WaveDecode is an SDR-focused signal processing project that converts raw IQ samples into actionable signal intelligence through FFT analysis, signal detection, visualization, and classification workflows.

The project is designed to provide a modular pipeline that can be extended for RF research, spectrum monitoring, protocol analysis, and machine learning applications.

Core Capabilities

Capability	Description
IQ Sample Processing	Ingestion and preprocessing of SDR IQ samples
Spectrum Analysis	FFT-based frequency-domain analysis
Signal Detection	Threshold-based signal identification
Waterfall Visualization	Time-frequency spectrum visualization
Signal Classification	Rule-based classification framework
SDR Integration	Support for RTL-SDR devices

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System Architecture

SDR Device / IQ Simulator
        │
        ▼
   IQ Sample Ingestion
        │
        ▼
   FFT Processing
        │
        ▼
   Signal Detection
        │
        ▼
   Classification Engine
        │
        ▼
   Visualization Layer

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Project Structure

WaveDecode/
├── src/
│   ├── iq_simulator.py
│   ├── fft_processor.py
│   ├── signal_detector.py
│   ├── spectrum_plot.py
│   └── classifier.py
│
├── requirements.txt
├── .gitignore
└── README.md

Components

Module	Responsibility
iq_simulator.py	Synthetic IQ sample generation
fft_processor.py	FFT computation and windowing
signal_detector.py	Signal detection logic
spectrum_plot.py	Spectrum and waterfall visualization
classifier.py	Signal classification framework

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Installation

Requirements

• Python 3.8+
• NumPy
• SciPy
• Matplotlib
• PyRTLSDR (optional)

Setup

git clone https://github.com/monika14r-m/WaveDecode.git

cd WaveDecode

pip install -r requirements.txt

RTL-SDR Support

sudo apt install rtl-sdr

pip install pyrtlsdr

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Usage

Spectrum Analysis with Simulated Signals

python src/spectrum_plot.py

Processing SDR Samples

from src.fft_processor import process_iq
from src.signal_detector import detect_signals

samples = capture_from_device(
    freq=433.92e6,
    sample_rate=2.4e6
)

fft_output = process_iq(samples)
signals = detect_signals(fft_output)

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Processing Pipeline

1. IQ Sample Acquisition

IQ samples are collected from either:

• RTL-SDR hardware
• Synthetic signal generators

2. Frequency Analysis

Fast Fourier Transform (FFT) is applied to convert time-domain samples into the frequency domain.

3. Signal Detection

Frequency peaks above configurable thresholds are identified as candidate transmissions.

4. Classification

Detected signals are passed through a rule-based classification engine that can later be extended with machine learning models.

5. Visualization

Results are displayed through spectrum plots and waterfall diagrams for interactive analysis.

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Development Roadmap

Phase 1: Signal Processing Foundation

• IQ sample simulator
• FFT processing pipeline
• Spectrum visualization

Phase 2: SDR Integration

• RTL-SDR device support
• Signal detection engine
• Waterfall visualization

Phase 3: Signal Intelligence

• Modulation classification
• Dataset generation tools
• Analysis dashboard

Phase 4: Machine Learning

• Automated signal classification
• Model training pipeline
• Performance benchmarking

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Project Status

Current Status: Active Development

Completed: Signal simulation, FFT processing, spectrum visualization

In Progress: RTL-SDR integration and signal detection

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Future Extensions

• GNU Radio integration
• Additional SDR hardware support
• Real-time streaming pipeline
• Protocol fingerprinting
• Signal clustering and anomaly detection
• Machine learning–based modulation recognition

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Contributing

Contributions are welcome.

git checkout -b feature/new-feature

pytest tests/

git commit -m "Add feature"

Please open an issue before submitting major changes.

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License

Licensed under the MIT License.

See the LICENSE file for additional information.