Loud Noise Detector

A Python system designed to detect and record significant noises in your home when you're away. Inspired by the need to monitor our pets when they're left alone at home for short periods, detecting barks or other sounds that might indicate distress or discomfort. It's also an ideal practical solution for parents who need to know if their baby is crying in another room. Perfect for any situation where you need to be informed about important sounds occurring in spaces where you can't be physically present.

🚀 Features

• Real-time detection of significant sounds (barking, crying, etc.)
• Customizable noise thresholds to match your needs
• Automatic recording when relevant sounds are detected
• Instant notification system
• Detailed event logging
• Easy to configure and customize

📋 Requirements

• Python 3.6 or higher
• PortAudio (for PyAudio) Ubuntu: sudo apt install python3-dev portaudio19-dev
• Working microphone

🔧 Installation

From PyPI

pip install loud-noise-detector

From Source

1. Clone the repository:

git clone https://github.com/Endika/loud-noise-detector.git
cd loud-noise-detector

2. Create and activate a virtual environment:

python -m venv .venv
# On Windows
.\.venv\Scripts\activate
# On Unix or MacOS
source .venv/bin/activate

3. Install dependencies:

pip install -r requirements.txt

4. Install development dependencies (optional):

pip install -r dev-requirements.txt

⚙️ Configuration

Command Line Arguments

Configuration File Options

🔐 Environment Setup

Generate your .env file with all necessary configurations:

# Generate .env file
cat > .env << EOL
# Slack configuration (required for Slack notifications)
SLACK_TOKEN=xoxb-your-token-here
SLACK_CHANNEL=CHANNEL-ID-HERE
EOL

Environment Variables Description

Note: For Slack notifications, you'll need to:

1. Create a Slack App in your workspace
2. Add chat:write and files:write OAuth scopes
3. Install the app to your workspace
4. Copy the Bot User OAuth Token to SLACK_TOKEN
5. Copy the Channel ID to SLACK_CHANNEL

Configuration Examples

For Pet Monitoring

threshold: 0.2 # Higher threshold for louder sounds like barking
cooldown_seconds: 30 # Longer cooldown to avoid too many notifications
seconds_to_record: 10 # Longer recording to capture context
pre_buffer_seconds: 3 # Capture sound before the bark
notifier_options:
  slack_channel: CHANNEL-ID-HERE

For Baby Monitoring

threshold: 0.15 # Lower threshold to detect crying
cooldown_seconds: 10 # Shorter cooldown for more frequent checks
seconds_to_record: 5 # Shorter recording duration
pre_buffer_seconds: 1 # Less pre-recording needed
notifier_options:
  slack_channel: CHANNEL-ID-HERE

You can create a configuration file in either YAML or JSON format. Place it in the config/ directory or specify its location using the --config argument.

🎯 Usage

Basic Usage

# Start the noise detector with default settings
loud-noise-detector

# Start with custom configuration file
loud-noise-detector --config path/to/config.yml

# Run in debug mode
loud-noise-detector --debug

As a Python Module

python -m src.main

📊 Development

Running Tests

# Run all tests
make test

Code Quality

# Run linting
make lint

# Run type checking
make quality

🤝 Contributing

This project uses Semantic Release for versioning.

Commit Message Format

Your commit messages must follow this format to trigger automatic version updates:

Development Workflow

1. Fork the repository
2. Create your feature branch (git checkout -b feature/amazing-feature)
3. Make your changes
4. Commit with appropriate message format
5. Push to your branch (git push origin feature/amazing-feature)
6. Open a Pull Request

📝 License

This project is licensed under the MIT License - see the LICENSE file for details.

🙏 Acknowledgments

• Thanks to all contributors who have helped shape this project
• Built with Python and PyAudio
• Inspired by the need for reliable noise monitoring solutions

📫 Contact

Endika Iglesias - endika2@gmail.com

Project Link: https://github.com/Endika/loud-noise-detector