A Visual representation tool for Physics - Sound Waves analysis using Python. This application leverages the latest features and libraries to provide an intuitive interface for analyzing sound waves and generating detailed visualizations. This program is a terminal-based application that allows users to interactively select audio files, perform various analyses, and visualize the results in a clean and professional manner.
- Arrow Key Navigation - Navigate through files and options with ↑ & ↓ keys
- Interactive File Selection - Visual selection with real-time highlighting
- Clean Interface - Professional terminal interface with clear visual feedback
- Intuitive Controls - Enter to select, 'q' to quit, Esc for quick exit
- Waveform Visualization - Time-domain amplitude analysis
- Frequency Spectrogram - Detailed frequency analysis over time
- Comprehensive dB Analysis - Peak, RMS, dynamic range calculations
- Multi-format Support - Handles mono and stereo WAV files
- Export Options - Save visualizations as high-quality PNG files
- Modular Design - Clean separation of concerns
- Scalable Structure - Easy to extend and maintain
- Import Optimization - Each module imports only what it needs
- Error Handling - Robust error management and user feedback
# Clone the repository
git clone https://github.com/TorresjDev/Python-Sound-Wave-Analysis.git
cd Python-Sound-Wave-Analysis
# Install dependencies
pip install -r requirements.txtPlace your .wav files in the data/ directory:
# Your structure should look like this:
data/
├── your_audio_file1.wav
├── your_audio_file2.wav
└── sample_audio.wavpython main.py🎮 Interactive Experience:
- Use ↑/↓ arrow keys to navigate through WAV files
- Press Enter to select your desired file
- Choose analysis options with the same intuitive navigation
- Enjoy beautiful visualizations and detailed analysis!
Python-Sound-Wave-Analysis/
│
├── main.py # 🚀 Clean entry point (main function only)
├── requirements.txt # 📦 Project dependencies
├── LICENSE # ⚖️ MIT License
├── README.md # 📖 Complete documentation
├── .gitignore # 🙈 Git ignore rules
│
├── sound_analysis/ # 🏗️ Core analysis package
│ ├── analyzer.py # 🔬 WAV processing & analysis engine
│ ├── tools.py # 🛠️ Math functions & user interface
│ └── visualization.py # 🎨 Matplotlib plotting functions
│
├── data/ # 📊 Your audio files go here
│ ├── sample1.wav # 🎵 Sample audio files
│ ├── sample2.wav
│ └── your_files.wav
│
├── figures/ # 📈 Generated visualizations
│ └── [auto-generated plots]
│
└── notebooks/ # 📓 Jupyter analysis notebooks
└── sound-wave-analysis.ipynb
- Single entry point with clean
main()function - Orchestrates the entire application flow
- Imports from modular components
- Mathematical audio processing functions
- Enhanced user interface with arrow-key navigation
- File management utilities
- Fallback support for different environments
- Core WAV file processing
- Comprehensive audio analysis algorithms
- Error handling and data validation
- Integration with visualization components
- All matplotlib-based plotting functions
- High-quality visualization generation
- Export capabilities for figures
- Multiple plot types (waveform, spectrogram, combined)
- Waveform visualization
- Frequency spectrogram
- Real-time display
- High-resolution PNG exports
- Organized in
figures/directory - Publication-ready quality
- Detailed numeric analysis
- No plot generation
- Fast processing mode
- ✅ WAV files (16-bit, mono/stereo)
- ✅ Various sample rates
- ✅ Multiple bit depths
- 🔊 Average dB - Overall sound level
- 📊 RMS dB - Root Mean Square analysis
- 📏 Dynamic Range - Peak-to-minimum difference
- 📈 Peak dB - Maximum amplitude
- 📉 Minimum dB - Quietest detected level
numpy>=1.21.0 # Numerical computations
matplotlib>=3.5.0 # Visualization engine
scipy>=1.7.0 # Signal processing
keyboard>=0.13.5 # Enhanced user interface
wave>=0.0.2 # Audio file handling- 🌊 Waveform - Visual representation of amplitude over time
- 📏 Amplitude - Wave height representing loudness/volume
- 🎵 Frequency - Wave cycles per second (Hz), determines pitch
- 🔊 Decibels (dB) - Logarithmic measure of sound intensity
- 📊 Spectrogram - Time-frequency analysis showing pitch changes
- 🎛️ Dynamic Range - Difference between loudest and quietest parts
- Clean, readable Python code
- Comprehensive documentation
- Type hints and docstrings
- Modular architecture
- Intuitive navigation
- Clear error messages
- Graceful error handling
- Cross-platform compatibility
- Optimized audio processing
- Efficient memory usage
- Fast visualization rendering
- Scalable for large files
🌊 Welcome to Sound Wave Analysis!
========================================
🎵 Available WAV files:
==================================================
Use ↑/↓ arrows to navigate, Enter to select, 'q' to quit
==================================================
► your_audio.wav ◄
another_file.wav
sample_music.wav
✅ Selected: your_audio.wav
🎵 Analysis Results
========================================
📁 File: your_audio.wav
📊 Sample Rate: 44,100 Hz
⏱️ Duration: 3.45 seconds
🎧 Channels: 2 (Stereo)
📈 Total Samples: 152,460
📈 Sound Levels:
🔊 Average dB: 85.32
📊 RMS dB: 142.18
📏 Dynamic Range: 68.45 dB
📈 Max dB: 195.67
📉 Min dB: 127.22
🎨 Generating visualizations...
✅ Analysis completed!
We welcome contributions! Please:
- Fork the repository
- Create a feature branch
- Follow the modular architecture
- Add comprehensive documentation
- Submit a pull request
This project is licensed under the MIT License - see the LICENSE file for details.
- Python Audio Community for excellent libraries
- NumPy & SciPy for powerful numerical computing
- Matplotlib for beautiful visualizations
- Keyboard Library for enhanced user interaction
🎵 Happy Audio Analysis! 🌊
Built with ❤️ by TorresjDev