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

An open and practical guide to Edge Audio.
https://github.com/afondiel/edge-audio

computer-audition edge-ai edge-audio edge-computing edge-devices embedded-ai embedded-audio

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An open and practical guide to Edge Audio.

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README 

          
[![](https://img.shields.io/badge/Contribute-Welcome-green)](./CONTRIBUTING.md)



# Edge Audio :studio_microphone: | A Practical Guide



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



## New to Edge AI? 



- Start with the [Edge AI Engineering](https://github.com/afondiel/edge-ai-engineering): a practical guide covering core concepts of the entire [Edge AI MLOps](https://docs.edgeimpulse.com/docs/concepts/edge-ai-fundamentals/what-is-edge-mlops) stack with industry blueprints.

- Then read this: [The Next AI Frontier is at the Edge](https://afondiel.github.io/posts/the-next-ai-frontier-is-at-the-edge/)

- Related work: [Edge Vision](https://github.com/afondiel/edge-vision)



## Table of Contents

- [Introduction](#introduction)

- [Project Structure](#project-structure)

- [Getting Started](#getting-started)

- [Contributing](#contributing)

- [License](#license)

- [Resources](#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:

```bash

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

```

2. Explore the [Edge AI Engineering](#edge-ai-engineering) section for foundational knowledge.

3. Dive into [Industry Blueprints](#key-concepts) for hands-on, sector-specific audio AI guides.

4. Use the [Edge Optimization Lab](#key-concepts)  and [Production Pipeline](#key-concepts) for deployment and scaling.



## Contributing



See [CONTRIBUTING.md](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](https://github.com/afondiel/computer-science-notebook/tree/master/core/ai-ml/computer-audition)

- [The Hugging Face Course on Transformers for Audio](https://github.com/huggingface/audio-transformers-course)



Books:

- [Machine Learning Systems: Principles and Practices of Engineering Artificially Intelligent Systems (Vijay Janapa Reddi)](https://mlsysbook.ai/)



[Back to the Top](#table-of-contents)