Gunshot Detection with Mel-frequency Cepstral Coefficients (MFCCs)

Introduction

This project focuses on the development of an audio-based gunshot detection system using Mel-frequency cepstral coefficients (MFCCs). The goal is to accurately classify audio recordings as either "gunshot" or "non-gunshot." This README provides a comprehensive overview of the project, from feature extraction to hardware integration.

Table of Contents

• Project Summary
• Prerequisites
• Feature Extraction
• MFCC Coefficients
• Hardware Integration
• Usage
• Example Code
• References

Project Summary

Objective

The primary objective of this project is to implement a gunshot detection system that can process real-time audio inputs and classify them as either "gunshot" or "non-gunshot." The system leverages audio features known as Mel-frequency cepstral coefficients (MFCCs) to capture the spectral characteristics of audio signals.

Key Steps

1. Data Collection: Collect a labeled dataset of audio recordings, including both gunshot and non-gunshot sounds.

2. Feature Extraction: Utilize the librosa library in Python to extract MFCC features from audio data, summarizing the spectral content.

3. Model Training: Train a machine learning model (e.g., neural network) on the extracted MFCC features using the labeled dataset.

4. Real-time Detection: Implement a real-time audio processing system that captures incoming audio, extracts MFCC features, and classifies the audio as "gunshot" or "non-gunshot" based on the trained model.

5. Alerting Mechanism: Implement an alerting or notification mechanism to respond when a gunshot sound is detected.

6. Testing and Evaluation: Evaluate the performance of the system using metrics like accuracy, precision, recall, and F1-score.

7. Hardware Integration: Integrate cost-effective hardware components (microphones, single-board computers, etc.) for real-time audio capture and processing.

Prerequisites

Before starting the project, ensure you have the following prerequisites:

• Python environment with required libraries (e.g., NumPy, librosa)
• Audio dataset (for training or comparison)
• Recorded audio files (for feature extraction)
• Hardware components for audio capture and processing (microphones, single-board computers, etc.)

Feature Extraction

To extract MFCCs from audio data, follow these steps:

1. Load and Preprocess Audio: Load the audio data and preprocess it (e.g., resample, normalize) for compatibility with feature extraction tools.

2. MFCC Calculation: Use librosa to calculate MFCCs. These coefficients capture the spectral characteristics of the audio signal.

3. Feature Storage: Store the extracted MFCCs as needed. You can save them as CSV files or NumPy arrays for later use.

MFCC Coefficients

MFCCs are typically labeled as MFCC1, MFCC2, and so on up to MFCC13. Each coefficient has a specific meaning:

• MFCC1: Represents overall energy or loudness of the audio frame.
• MFCC2 to MFCC13: Capture spectral content across different frequency bands, providing information about the distribution of energy in each band.

These coefficients collectively provide a compact representation of the audio's spectral characteristics.

Hardware Integration

Integrating hardware components into the project is essential for real-time audio capture and processing. Consider the following:

• Microphones: Choose suitable microphones for audio capture, balancing cost and quality.
• Single-Board Computers (SBCs): Use SBCs like Raspberry Pi for audio processing.
• Audio Interfaces: Consider USB audio interfaces for multiple microphone support.
• Power Supply: Ensure a reliable power source for continuous operation.
• Case and Mounting: Protect hardware components with suitable cases and mounts.
• Networking: Include network connectivity for remote monitoring and data transfer.

Usage

You can use the extracted MFCCs for various audio-related tasks, including gunshot detection, speech recognition, and more. The choice of features depends on your specific application and audio data characteristics.

Example Code

Check the provided Python code examples for extracting MFCCs from audio data and saving them in different formats. Additionally, explore hardware integration code samples for real-time audio processing.

References

For more information on MFCCs, audio feature extraction, and hardware integration, refer to the following resources:

• Librosa Documentation
• Mel-frequency cepstrum - Wikipedia