Dynamic Audio Classification with Neural Networks

Live dynamic audio signal classification and deployment with neural networks

This project developed a comprehensive system for live dynamic audio signal classification using neural networks and Mel-Frequency Cepstral Coefficients (MFCC). The system enables real-time audio classification for security applications through automated machine learning deployment on embedded hardware.

The project utilized the Edge Impulse framework to create a modular, scalable solution for audio classification that can be deployed on personal devices for continuous monitoring and analysis. By combining MFCC feature extraction with neural network classification, the system achieved 81.4% accuracy on the UrbanSound8K dataset for security-relevant audio classes.

Table of Contents

  1. Project Overview
  2. Technical Approach
  3. Methodology
  4. Implementation Details
  5. Results and Analysis
  6. Deployment and Testing
  7. Learning Outcomes
  8. Project Impact

Project Overview

Primary Objectives:

  1. Develop modular audio classification system: Create scalable neural network architecture for real-time audio processing
  2. Implement MFCC feature extraction: Utilize Mel-Frequency Cepstral Coefficients for optimal audio representation
  3. Deploy embedded solution: Raspberry Pi implementation for practical security applications
  4. Achieve high classification accuracy: Target 80%+ accuracy on UrbanSound8K dataset

Key Innovation: This project extended beyond typical academic exercises by implementing real-time audio classification with embedded deployment, providing practical insights into machine learning applications for security and monitoring systems.

Technical Stack:

  • Python Libraries: Librosa (MFCC), Keras (Neural Networks)
  • Framework: Edge Impulse for model development and deployment
  • Hardware: Raspberry Pi with USB microphone
  • Dataset: UrbanSound8K with four audio classes

Technical Approach

MFCC Feature Extraction

The project utilized Mel-Frequency Cepstral Coefficients (MFCC) for audio feature extraction, which transforms raw audio signals into frequency-domain representations optimized for human auditory perception.

Mathematical Foundation: The MFCC process involves several key steps:

  1. Fast Fourier Transform (FFT): Converts time-domain signals to frequency domain
  2. Mel-scale Filtering: Applies human auditory system modeling
  3. Logarithmic Compression: Emphasizes important frequency components
  4. Discrete Cosine Transform: Reduces dimensionality while preserving information

MFCC Equation:

MFCC Mathematical Equation: Core mathematical foundation for audio feature extraction

Implementation Benefits:

  • Dimensionality Reduction: Efficient representation of audio features
  • Human Auditory Modeling: Optimized for natural sound classification
  • Noise Robustness: Improved performance in varying acoustic environments
  • Real-time Processing: Suitable for live audio classification
Neural Network Architecture

The neural network implementation used Keras to create an interconnected web of neurons for audio classification.

Network Structure:

  • Input Layer: MFCC coefficient matrix (frequency × time)
  • Hidden Layers: Multiple dense layers with activation functions
  • Output Layer: Softmax classification for four audio classes
  • Training Cycles: 300 epochs for optimal convergence
Single Neuron Architecture: Basic building block of neural networks with inputs, processing logic, and output

Key Parameters:

  • Learning Rate: 0.00005 (prevents overfitting)
  • Confidence Threshold: 0.70 (classification certainty)
  • Window Size: 1000ms (audio analysis window)
  • Window Increase: 100ms (sliding window increment)
Edge Impulse Framework

The project leveraged Edge Impulse for streamlined model development and deployment.

Framework Benefits:

  • Graphical Interface: User-friendly model development
  • Automated Feature Generation: MFCC processing pipeline
  • Model Training: Integrated neural network training
  • Hardware Deployment: Direct deployment to embedded systems

Workflow Integration:

  1. Data Upload: Automated dataset processing
  2. Feature Extraction: MFCC coefficient generation
  3. Model Training: Neural network optimization
  4. Deployment: Hardware-ready model export
Interconnected Neural Network: Complex web of neurons working together for audio classification

Methodology

Data Collection and Preparation

Dataset Selection:

  • UrbanSound8K: Comprehensive urban audio dataset with 8,732 labeled sound excerpts from 10 classes
  • Security Classes: car_horn, dog_bark, gun_shot
  • Control Class: air_conditioner (ambient noise)
  • Data Split: 75% training, 25% testing

Automated Data Upload System: The project implemented a sophisticated Python script for automated dataset processing and Edge Impulse integration:

  • Librosa Integration: Audio file processing and MFCC feature extraction
  • Edge Impulse API: Automated upload to cloud-based development platform
  • Metadata Processing: CSV-based label assignment and organization
  • Quality Control: Audio format validation and preprocessing
  • Batch Processing: Efficient handling of large audio datasets

Key Script Features:

  • WAV File Processing: 16kHz mono signal conversion for optimal processing
  • CBOR Encoding: Efficient data serialization for API transmission
  • HMAC Authentication: Secure API communication with cryptographic signatures
  • Error Handling: Robust failure detection and reporting system

Audio Processing Pipeline:

  1. File Upload: Automated script for Edge Impulse integration
  2. Metadata Processing: Label assignment and organization
  3. Quality Control: Audio file validation and preprocessing
  4. Feature Extraction: MFCC coefficient generation
Model Development Process

Step 1: Data Upload and Organization

  • Automated Python script for Edge Impulse integration
  • Metadata file processing for proper labeling
  • Training/testing split configuration (25% testing)

Step 2: Feature Engineering

  • Time series data block configuration
  • Window size optimization (1000ms)
  • Window increase parameter (100ms)
  • MFCC processing block implementation

Step 3: Neural Network Training

  • 300 training cycles for optimal convergence
  • Learning rate optimization (0.00005)
  • Confidence threshold setting (0.70)
  • Overfitting prevention strategies

Step 4: Model Validation

  • Confusion matrix analysis
  • Accuracy assessment on test dataset
  • False positive/negative evaluation
  • Performance optimization
Deployment Strategy

Hardware Configuration:

  • Raspberry Pi: Primary deployment platform
  • USB Microphone: Audio input device
  • Real-time Processing: Continuous audio monitoring
  • Classification Output: Probabilistic class assignments

System Architecture:

Audio Input → MFCC Processing → Neural Network → Classification Output

Implementation Details

Audio Signal Processing Pipeline

The implementation followed a systematic approach to audio classification:

1. Signal Acquisition:

  • Real-time audio capture via USB microphone
  • Continuous sampling at appropriate frequency
  • Buffer management for processing windows

2. Feature Extraction:

  • MFCC coefficient calculation for each window
  • Frequency domain transformation
  • Dimensionality reduction for neural network input

3. Classification:

  • Neural network inference on extracted features
  • Probabilistic class assignment
  • Confidence threshold filtering

4. Output Generation:

  • Real-time classification results
  • Confidence level reporting
  • Continuous monitoring capability
Technical Specifications

Audio Processing Parameters:

  • Sampling Rate: 44.1 kHz
  • Window Size: 1000ms
  • Window Overlap: 100ms
  • MFCC Coefficients: 13 coefficients per window

Neural Network Configuration:

  • Input Shape: MFCC coefficient matrix
  • Hidden Layers: Dense layers with ReLU activation
  • Output Layer: Softmax for multi-class classification
  • Training Epochs: 300 cycles

Performance Metrics:

  • Accuracy: 81.4% on test dataset
  • Processing Latency: Real-time classification
  • Memory Usage: Optimized for embedded deployment
  • Power Consumption: Efficient for continuous operation

Results and Analysis

Model Performance

Confusion Matrix Analysis: The trained model achieved excellent classification performance across all audio classes:

  • Car Horn: High accuracy with minimal false positives
  • Dog Bark: Robust classification with clear feature distinction
  • Gun Shot: Reliable detection for security applications
  • Air Conditioner: Control class for ambient noise assessment
Neural Network Confusion Matrix: Classification results showing 81.4% accuracy across four audio classes

Accuracy Metrics:

  • Overall Accuracy: 81.4% on test dataset
  • False Positive Rate: Minimal across all classes
  • False Negative Rate: Low for security-critical sounds
  • Confidence Distribution: Well-calibrated probability outputs
Feature Analysis

MFCC Coefficient Visualization: The spectrogram plots revealed distinct frequency patterns for each audio class:

  • Car Horn: Concentrated high-frequency components
  • Dog Bark: Broad frequency spectrum with characteristic peaks
  • Gun Shot: Sharp, transient frequency signatures
  • Air Conditioner: Continuous, low-frequency background noise

3D Feature Representation: The MFCC coefficients formed distinct clusters for each audio class, enabling effective neural network classification.

MFCC 3D Feature Visualization: 11,402 total samples showing distinct frequency patterns for audio classification
Real-world Testing

Deployment Results:

  • Live Classification: Successful real-time audio processing
  • Environmental Adaptation: Robust performance in varying conditions
  • False Positive Management: Effective filtering of ambient noise
  • Security Application: Reliable detection of critical sounds

Performance Validation:

  • Raspberry Pi Deployment: Successful embedded implementation
  • Continuous Operation: 24/7 monitoring capability
  • Resource Efficiency: Optimized for embedded hardware
  • Scalability: Modular design for different applications

Deployment and Testing

Hardware Implementation

Raspberry Pi Configuration:

  • Model: Raspberry Pi with USB microphone
  • Operating System: Linux-based deployment
  • Audio Interface: USB microphone for continuous input
  • Processing: Real-time MFCC and neural network inference

System Integration:

Microphone → Audio Processing → MFCC Extraction → Neural Network → Classification Output
Testing Methodology

Validation Process:

  1. Offline Testing: Dataset validation and accuracy assessment
  2. Live Testing: Real-time audio classification testing
  3. Environmental Testing: Performance in varying acoustic conditions
  4. Long-term Testing: Continuous operation validation

Performance Metrics:

  • Classification Accuracy: 81.4% on test data
  • Real-time Latency: Sub-second classification response
  • False Positive Rate: Minimal for security applications
  • System Reliability: Continuous operation capability
Challenges and Solutions

Technical Challenges:

  • Ambient Noise Classification: Air conditioner sounds resembled background noise
  • Low Decibel Detection: Difficulty with quiet sounds
  • Frequency Overlap: Similar frequency patterns between classes

Solutions Implemented:

  • Enhanced Feature Extraction: Improved MFCC processing
  • Confidence Thresholding: Better classification filtering
  • Environmental Adaptation: Robust performance in varying conditions

Learning Outcomes

This project significantly enhanced my technical and professional development:

Audio Signal Processing:

  • MFCC Implementation: Deep understanding of audio feature extraction
  • Frequency Domain Analysis: Mastery of FFT and signal processing
  • Real-time Processing: Experience with live audio classification
  • Audio Quality Assessment: Understanding of audio preprocessing requirements

Machine Learning Expertise:

  • Neural Network Design: Advanced neural network architecture development
  • Feature Engineering: MFCC coefficient optimization for classification
  • Model Training: Hyperparameter tuning and overfitting prevention
  • Performance Evaluation: Comprehensive model validation and testing

Embedded Systems Development:

  • Hardware Integration: Raspberry Pi deployment and configuration
  • Real-time Systems: Continuous audio monitoring implementation
  • Resource Optimization: Efficient processing for embedded platforms
  • Deployment Strategies: Production-ready system implementation

Professional Development:

  • Project Management: Systematic approach to complex audio processing projects
  • Technical Documentation: Comprehensive project reporting and analysis
  • Problem-solving Skills: Debugging and optimization in audio classification
  • Research Methodology: Experimental design and validation procedures

Project Impact

This dynamic audio classification project demonstrated the practical application of machine learning in embedded systems, providing:

Technical Contributions:

  • Modular Audio Classification: Scalable system for various applications
  • Real-time Processing: Live audio monitoring and classification
  • Embedded Deployment: Practical implementation on Raspberry Pi
  • Security Applications: Reliable detection of critical audio events

Innovation in Audio Processing:

  • MFCC Optimization: Enhanced feature extraction for classification
  • Neural Network Architecture: Efficient audio classification models
  • Edge Computing: Local processing for privacy and reliability
  • Continuous Monitoring: 24/7 audio surveillance capability

Practical Applications:

  • Security Systems: Automated threat detection through audio
  • Smart Home Integration: Intelligent audio monitoring
  • Industrial Monitoring: Equipment sound analysis
  • Accessibility: Audio-based environmental awareness

The project established a foundation for real-time audio classification systems that can be deployed in various security and monitoring applications. The combination of MFCC feature extraction, neural network classification, and embedded hardware deployment provides a complete solution for automated audio analysis.

This project was completed as part of MATH 495 (Mathematical Modeling) at Iowa State University.