📡 Speech Signal Modulation and Demodulation Project

🎵 Advanced Telecommunication System Implementation 🎵

A complete Python-based communication system demonstrating amplitude modulation, quadrature components, and signal recovery techniques

Project Partner: Inshaf

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🌟 Project Highlights

🚀 Real-world Application: Simulates actual telecommunication systems
🎛️ Multiple Modulation: Implements AM with quadrature components
📊 Signal Analysis: Complete visualization and frequency domain analysis
🔄 Robust Recovery: Tests demodulation under various carrier conditions
📱 Easy to Use: Interactive Jupyter notebook with step-by-step execution

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� Table of Contents

• 🌟 Project Highlights
• 📡 Overview
• 🔧 System Architecture
• 📐 Mathematical Model
• ⚙️ Technical Specifications
• 🚀 Quick Start
• 📊 Input & Output Files
• 🛠️ Dependencies & Tools

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🎬 Quick Demo

Input → Processing → Output

🎤 Speech Files (ziad.wav, esoo.wav, mohey.wav)
    ↓ [Resample to 250kHz]
📡 Modulation [s(t) = x₁(t)cos(ω₁t) + x₂(t)cos(ω₂t) + x₃(t)sin(ω₂t)]
    ↓ [Transmission Simulation]
🔍 Demodulation [Standard + Phase/Frequency Shifted]
    ↓ [Low-Pass Filtering]
🎵 Recovered Audio Files (18 output variations)

Results You'll Get:

• 📈 Signal Plots: Time and frequency domain visualizations
• 🎵 Audio Files: 18 processed audio outputs showing different recovery methods
• 📊 Analysis: Complete comparison of demodulation techniques
• ⚡ Performance: Real-time processing and quality assessment

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�📡 Overview

This project demonstrates speech signal modulation and demodulation techniques using Python. It implements a complete communication system that processes three audio files, modulates them using different carrier frequencies, transmits the combined signal, and then demodulates to recover the original signals.

🎯 Key Features

• 🎵 Audio Processing: Resampling and preprocessing of speech signals
• 📶 Signal Modulation: Amplitude modulation with quadrature components
• 🔄 Multiple Demodulation: Standard, phase-shifted, and frequency-shifted recovery
• 📊 Visualization: Complete signal analysis and plotting
• 💾 Audio Generation: Output of processed audio files

🚀 What This Project Does

1. Input: Reads three speech audio files (.wav format)
2. Processing: Resamples all signals to 250,000 Hz and equalizes lengths
3. Modulation: Combines signals using the mathematical model below
4. Transmission: Simulates transmission of the modulated signal
5. Demodulation: Recovers original signals with various carrier configurations
6. Output: Generates processed audio files for analysis

🔧 System Architecture

System Components:

• 🎤 Input Stage: Three speech signals ready for modulation
• 📡 Modulation Stage: Signals multiplied with carrier waves
• ➕ Combiner: All modulated signals summed to create s(t)
• 📶 Transmission: Combined signal represents transmitted data
• 🔍 Demodulation Stage: Signal recovery using coherent detection
• 🔽 Low-Pass Filters (L.P.F): Remove high-frequency components
• 🎵 Output Stage: Recovered speech signals

🎛️ Demodulation Variations

• Standard: Perfect carrier synchronization
• Phase-shifted: Carriers with 10°, 30°, 90° phase offsets
• Frequency-shifted: Carriers with 2 Hz, 10 Hz frequency offsets

📐 Mathematical Model

The core of this project is based on the following modulation equation:

s(t) = x₁(t)cos(ω₁t) + x₂(t)cos(ω₂t) + x₃(t)sin(ω₂t)

Where:

• s(t) → Composite modulated signal (transmitted)
• x₁(t), x₂(t), x₃(t) → Input speech signals (ziad.wav, esoo.wav, mohey.wav)
• ω₁, ω₂ → Carrier frequencies
• cos(ω₁t), cos(ω₂t) → Cosine carriers for signals 1 and 2
• sin(ω₂t) → Sine carrier for signal 3 (quadrature component)

⚙️ Technical Specifications

• 📡 Modulation: Amplitude Modulation (AM) with Quadrature Components
• 🔊 Sampling Rate: 250,000 Hz
• 🎛️ Filter Type: Butterworth Low-Pass Filter
• 📐 Carrier Types: Cosine and Sine waves
• 🔄 Processing: Real-time signal visualization and analysis

Project Structure

├── project.ipynb          # Main Jupyter notebook
├── signals/               # Input audio files
│   ├── esoo.wav
│   ├── mohey.wav
│   └── ziad.wav
├── output_signals/        # Generated output files
│   ├── Out_1.wav
│   ├── Out_1_phase_10.wav
│   ├── Out_1_phase_30.wav
│   ├── Out_1_phase_90.wav
│   ├── Out_1_shift_2.wav
│   ├── Out_1_shift_10.wav
│   └── ... (similar files for signals 2 and 3)
├── images/                # Documentation images
│   └── Block diagram.png
└── requirements.txt       # Python dependencies

🚀 Quick Start

Prerequisites

• Python 3.7+
• Jupyter Notebook or JupyterLab

📥 Installation

1. Clone the repository:

   git clone https://github.com/mohamed-munsif/speech-signal-modulation.git
   cd speech-signal-modulation

2. Install dependencies:

   pip install -r requirements.txt

🎮 Usage

1. Prepare Input: Place your .wav audio files in the signals/ folder
2. Run Analysis: Open project.ipynb and execute all cells
3. View Results: Check output_signals/ for generated files
4. Analyze: Review plots and audio outputs

📊 Input & Output Files

📥 Input Files (signals/ folder)

• ziad.wav → Speech signal 1 (modulated with cos(ω₁t))
• esoo.wav → Speech signal 2 (modulated with cos(ω₂t))
• mohey.wav → Speech signal 3 (modulated with sin(ω₂t))

📤 Output Files (output_signals/ folder)

• Out_X.wav → Standard demodulated signals
• Out_X_phase_Y.wav → Phase-shifted demodulated signals (Y = 10°, 30°, 90°)
• Out_X_shift_Y.wav → Frequency-shifted demodulated signals (Y = 2Hz, 10Hz)

Where X = 1, 2, 3 corresponding to the three input signals

🛠️ Dependencies & Tools

Core Libraries

• numpy → Numerical computations and array operations
• scipy → Signal processing and filtering
• librosa → Audio processing and resampling
• soundfile → Audio file I/O operations
• matplotlib → Signal plotting and visualization
• ipython → Interactive Python environment

Installation

All dependencies are listed in requirements.txt for easy installation.

🤝 Contributing

Feel free to submit issues and enhancement requests!

📞 Contact

For questions about this telecommunication project, feel free to reach out!