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

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

The first part of this project gave us the prerequisites to train a music genre recognition model. Now that we have obtained the dataset and have gotten acquainted with implementing the MFCCs feature extraction, we can delve into the model design and the application deployment. Although we might consider leaving the deployment for the end, it should never be the case when building tinyML applications. Given its limited computational and memory capabilities, the target device must always be at the center of our design choice, from the feature extraction to the model design.

For this reason, this second part will amply discuss how the target device influences the implementation of the MFCCs feature extraction.

We will start our discussion by tailoring the MFCCs implementation for the Raspberry Pi Pico. Here, we will learn how fixed-point arithmetic can help minimize the latency performance and...