You're reading from TinyML Cookbook - Second Edition

Product type Book

Published in Nov 2023

Publisher Packt

ISBN-13 9781837637362

Pages 664 pages

Edition 2nd Edition

Languages

Concepts

Machine Learning

Author (1):

Gian Marco Iodice

Table of Contents (16) Chapters

Preface

1. Getting Ready to Unlock ML on Microcontrollers

2. Unleashing Your Creativity with Microcontrollers

3. Building a Weather Station with TensorFlow Lite for Microcontrollers

4. Using Edge Impulse and the Arduino Nano to Control LEDs with Voice Commands

5. Recognizing Music Genres with TensorFlow and the Raspberry Pi Pico – Part 1

6. Recognizing Music Genres with TensorFlow and the Raspberry Pi Pico – Part 2

7. Detecting Objects with Edge Impulse Using FOMO on the Raspberry Pi Pico

8. Classifying Desk Objects with TensorFlow and the Arduino Nano

9. Building a Gesture-Based Interface for YouTube Playback with Edge Impulse and the Raspberry Pi Pico

10. Deploying a CIFAR-10 Model for Memory-Constrained Devices with the Zephyr OS on QEMU

11. Running ML Models on Arduino and the Arm Ethos-U55 microNPU Using Apache TVM

12. Enabling Compelling tinyML Solutions with On-Device Learning and scikit-learn on the Arduino Nano and Raspberry Pi Pico

13. Conclusion

14. Other Books You May Enjoy

15. Index

Acquiring audio data with a smartphone

As with all ML problems, data acquisition is the first step to take, and Edge Impulse offers several ways to do this directly from the web browser.

In this first recipe, we will learn how to acquire audio samples using a mobile phone.

Getting ready

Edge Impulse offers a straightforward and efficient method for data acquisition using smartphones through internet connectivity. This approach is so simple and intuitive that even people with no technical background will find it easy to use.

The only factor to consider before preparing the dataset is related to the number of audio recordings to take for training the model, outlined in the upcoming subsection.

Collecting audio samples for KWS

The number of samples depends entirely on the nature of the problem—therefore, no one approach fits all. In our scenario, 25 recordings for each class, each corresponding to the utterance to recognize (red, green...