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

Deploying the MFCCs feature extraction algorithm on the Raspberry Pi Pico

The final two recipes of this chapter will guide us through the development of the music genre classification application on the microcontroller.

In this particular recipe, we will deploy the MFCCs feature extraction algorithm using the CMSIS-DSP on the Raspberry Pi Pico.

Getting ready

Extracting MFCCs from raw audio data is the first stage of our computing chain to classify music genres. Since we developed this algorithm in Python using the CMSIS-DSP library, transitioning to a C language implementation should be relatively straightforward.

The C version of the CMSIS-DSP library mirrors its Python counterpart, offering the same functions and, often, an identical API, simplifying the migration to the final implementation on a board.

The only ingredients needed to deploy the MFCCs feature extraction algorithm on the Raspberry Pi Pico or other Arm-based microcontrollers are the following:

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