🎵 Acoustic Vehicle Detection & Classification

🚗 Advanced MATLAB Signal Processing System

This project implements a sophisticated MATLAB-based acoustic detection system that identifies and classifies passing vehicles (cars, trucks, motorbikes) using advanced signal processing techniques. The system combines microphone array processing with custom-trained deep neural networks to provide real-time audio analysis and classification with visual feedback.

Purpose: Real-time vehicle identification using microphone arrays and deep neural networks for traffic monitoring and analysis

🎥 System Demonstration

Watch the complete system demonstration showcasing real-time vehicle detection and classification:

A similar demonstration of this classification technique can be viewed in my YouTube demonstration.

🚀 Project Overview

This advanced signal processing system leverages MATLAB's deep learning capabilities and microphone array technology to create a real-time vehicle classification platform. By combining traditional signal processing techniques with modern machine learning approaches, the system achieves accurate vehicle detection and type identification through acoustic signature analysis.

💡Key Innovation: Integration of microphone array cross-correlation processing with custom-trained CNNs for robust real-time vehicle acoustic classification

🛠️ Technical Stack

Core Platform

MATLAB R2018b Signal Processing Toolbox Deep Learning Toolbox Audio System Toolbox Computer Vision Toolbox Real-time Processing

Signal Processing

Microphone Arrays Cross-correlation Analysis Spectrogram Generation Time-Frequency Analysis Audio Feature Extraction Noise Reduction

Machine Learning

Custom CNNs Detection Networks Classification Networks Model Optimization Training Pipeline Real-time Inference

Signal Processing Pipeline:
Audio Input → Microphone Array Processing → Cross-correlation Analysis → Spectrogram Generation → CNN Detection → Vehicle Classification → Probability Calculation → Visual Feedback → Screenshot Capture

🔧 Core Features

🎯 Real-time Classification

Instant vehicle type identification: No Vehicles, Car, Truck, or Motorbike with probability scoring and temporal smoothing for stable results.

🎤 Microphone Arrays

Multi-microphone cross-correlation processing for enhanced detection accuracy and spatial audio analysis capabilities.

🧠 Custom Neural Networks

Internally trained CNNs with proprietary dataset for vehicle audio classification, optimized for real-time performance.

📊 Live Visualization

Real-time probability plots and classification displays with webcam integration for comprehensive monitoring and documentation.

🔬 Research Foundation

📄 Academic Research

The microphone array processing methodology is based on established research in vehicle detection through pass-by noise analysis. The cross-correlation technique for signal evolution tracking between microphone pairs is detailed in:

📄 "Observation of Vehicle Axles Through Pass-by Noise: A Strategy of Microphone Array Design"

🎯 Traffic Monitoring Applications

🚦Traffic Analysis: Automated vehicle counting and classification for traffic management systems
🔊Noise Monitoring: Environmental noise assessment and vehicle noise pollution tracking
🛣️Highway Surveillance: Remote monitoring of highway traffic patterns and vehicle types
📊Data Collection: Long-term traffic statistics and pattern analysis for urban planning
🚨Security Applications: Perimeter monitoring and unauthorized vehicle detection

🛠️ Implementation Details

Signal Processing Architecture

Audio Capture: Real-time audio input via MATLAB's audioDeviceReader for live processing
File Processing: Support for pre-recorded audio files using audioread function
Cross-correlation Analysis: Multi-microphone signal evolution tracking for vehicle detection
Spectrogram Generation: Time-frequency domain conversion for CNN input preparation

Machine Learning Components

Detection CNN: Custom-trained network for vehicle presence detection using microphone arrays
Classification CNN: Single-microphone based neural network for vehicle type identification
Training Dataset: Internally recorded and manually labeled vehicle audio data
Model Optimization: MATLAB deep learning toolbox optimization for real-time performance

Visualization & Interface

Real-time Plotting: Dynamic probability displays for each vehicle class
Data Buffering: Temporal smoothing of classification results for stability
Webcam Integration: Screenshot capture during detection events for documentation
Error Handling: Robust audio format validation and sampling rate correction

📈 Technical Highlights

Advanced Signal Processing

Multi-channel Analysis: Sophisticated microphone array processing for enhanced spatial resolution
Real-time Performance: Optimized algorithms for low-latency vehicle detection and classification
Noise Robustness: Advanced filtering techniques for operation in various acoustic environments
Adaptive Processing: Dynamic parameter adjustment based on environmental conditions

Machine Learning

🧠Custom Architecture: Purpose-built CNN architectures for vehicle acoustic signatures
📊Training Methodology: Comprehensive dataset creation and validation procedures
⚡Real-time Inference: Optimized models for live classification with minimal latency
🎯High Accuracy: Robust classification performance across various vehicle types and conditions

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