Edge Audio 🎙️ | A Practical Guide

A practical guide for real-world and efficient AI audio applications for resource-constrained devices with industry standards in mind.

New to Edge AI?

• Start with the Edge AI Engineering: a practical guide covering core concepts of the entire Edge AI MLOps stack with industry blueprints.
• Then read this: The Next AI Frontier is at the Edge
• Related work: Edge Vision

Table of Contents

• Introduction
• Project Structure
• Getting Started
• Contributing
• License
• Resources

Introduction

The goal of this guide is to provide resources for building, optimizing, and deploying AI audio applications at the edge, through hands-on examples including practical notebooks and real-world use cases across key industries.

Key Concepts

Industry Blueprints

• Autonomous Systems
• Healthcare & Medical Imaging*
• Retail & Consumer Analytics
• Security & Surveillance
• Agriculture & Precision Farming
• Manufacturing & Quality Control
• Smart Cities & Urban Planning

Edge Optimization Lab: techniques and tools for maximizing performance and efficiency of audio models on edge hardware

• Model Quantization
• Pruning Techniques
• Federated Learning
• Compiler Targets
• Hardware-Specific Optimization

Production Pipelines: guides and templates for robust, scalable edge audio AI operations

• CI/CD for Edge
• Monitoring (Drift Detection, Edge Metrics Dashboard)
• OTA Updates
• Edge Security (Secure Boot, Data Encryption, Threat Detection, Privacy-Preserving Audio, Adversarial Robustness, Device Hardening, Compliance)

Reference Architectures: blueprints for edge audio hardware and system design

• Microphone Array Setups
• Edge Server Specs
• IoT Connectivity
• Edge-Cloud Hybrid Models

Integration

• Notebooks (hands-on deep dives)
• Companion Resources
• Industry-Specific Stardards

Project Structure

├── edge-ai-engineering/
│   ├── introduction-to-edge-ai.md
│   ├── edge-ai-architectures.md
│   ├── model-optimization-techniques.md
│   ├── hardware-acceleration.md
│   ├── edge-deployment-strategies.md
│   ├── real-time-processing.md
│   ├── privacy-and-security.md
│   ├── edge-ai-frameworks.md
│   └── benchmarking-and-performance.md    
├── industry-blueprints/
│   ├── autonomous-systems/
│   │   ├── voice-command-recognition-tflite.md
│   │   ├── siren-detection-jetson.md
│   │   └── acoustic-scene-understanding.md
│   ├── healthcare-medical-imaging/
│   │   ├── heart-sound-analysis-edge.md
│   │   ├── respiratory-event-detection.md
│   │   └── patient-monitoring-audio.md
│   ├── retail-consumer-analytics/
│   │   ├── customer-sentiment-analysis.md
│   │   ├── in-store-sound-event-detection.md
│   │   └── voice-assistant-embedded.md
│   ├── security-surveillance/
│   │   ├── gunshot-detection-edge.md
│   │   ├── glass-break-detection.md
│   │   └── anomaly-detection-public-places.md
│   ├── agriculture-precision-farming/
│   │   ├── livestock-sound-monitoring.md
│   │   ├── machinery-failure-detection.md
│   │   └── environmental-sound-classification.md
│   ├── manufacturing-quality-control/
│   │   ├── equipment-fault-detection-audio.md
│   │   ├── process-monitoring-sound.md
│   │   └── predictive-maintenance-audio.md
│   └── smart-cities-urban-planning/
│       ├── urban-noise-mapping-edge.md
│       ├── emergency-sound-detection.md
│       └── public-transport-announcement-monitoring.md
├── edge-optimization-lab/
│   ├── model-quantization/
│   │   ├── post-training-int8.md
│   │   └── qat-pytorch.md
│   ├── pruning-techniques/
│   │   ├── magnitude-pruning.md
│   │   └── lottery-ticket-hypothesis.md
│   ├── federated-learning/
│   │   ├── privacy-preserving-audio.md
│   │   └── distributed-training.md
│   ├── compiler-targets/
│   │   ├── tvm-tutorial.md
│   │   └── onnx-runtime-guide.md
│   └── hardware-specific-optimization/
│       ├── nvidia-jetson-optimization.md
│       ├── raspberry-pi-edge-audio.md
│       └── microcontroller-tinyml-audio.md
├── production-pipelines/
│   ├── ci-cd-for-edge.md
│   ├── monitoring/
│   │   ├── drift-detection.md
│   │   └── edge-metrics-dashboard.md
│   ├── ota-updates.md
│   └── edge-security/
│       ├── secure-boot-implementation.md
│       ├── data-encryption-edge.md
│       ├── threat-detection/
│       │   ├── abnormal-sound-alerts.md
│       │   └── tamper-detection.md
│       ├── privacy-preserving-audio/
│       │   ├── federated-learning-techniques.md
│       │   └── differential-privacy.md
│       ├── model-security/
│       │   └── adversarial-robustness.md
│       ├── edge-device-hardening/
│       │   ├── secure-deployment.md
│       │   └── secure-communication.md
│       └── industry-compliance/
│           ├── regulatory-standards.md
│           └── ethical-ai-guidelines.md
├── reference-architectures/
│   ├── microphone-array-setups.md
│   ├── edge-server-specs.md
│   ├── iot-connectivity.md
│   └── edge-cloud-hybrid-models.md
└── _integration/
    ├── cs-notebook-redirects.md
    ├── companion-resources.md
    └── industry-specific-regulations.md

Getting Started

1. Clone this repository:

git clone https://github.com/afondiel/edge-audio.git

2. Explore the Edge AI Engineering section for foundational knowledge.
3. Dive into Industry Blueprints for hands-on, sector-specific audio AI guides.
4. Use the Edge Optimization Lab and Production Pipeline for deployment and scaling.

Contributing

See CONTRIBUTING.md for guidelines on how to contribute, report issues, or suggest new blueprints.

License

Distributed under the MIT License. See LICENSE for more information.

Resources

• Computer Audio Notes
• The Hugging Face Course on Transformers for Audio

Books:

• Machine Learning Systems: Principles and Practices of Engineering Artificially Intelligent Systems (Vijay Janapa Reddi)

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