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TinyML Cookbook

You're reading from   TinyML Cookbook Combine machine learning with microcontrollers to solve real-world problems

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Product type Paperback
Published in Nov 2023
Publisher Packt
ISBN-13 9781837637362
Length 664 pages
Edition 2nd Edition
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Author (1):
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Gian Marco Iodice Gian Marco Iodice
Author Profile Icon Gian Marco Iodice
Gian Marco Iodice
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Toc

Table of Contents (16) Chapters Close

Preface 1. Getting Ready to Unlock ML on Microcontrollers FREE CHAPTER 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

Summary

In this chapter, we have completed our music genre recognition application on the Arduino Nano and Raspberry Pi Pico. Here, our focus has been optimizing the MFCCs feature extraction algorithm through software optimizations, using fixed-point arithmetic. Additionally, we have developed, trained, and deployed an RNN model that relies on the LSTM operator to classify music genres.

Our practical journey started by getting acquainted with fixed-point arithmetic and its role in accelerating the extraction of MFCCs from audio clips. To achieve this objective, the Python CMSIS-DSP played a crucial role, helping us to develop an algorithm in a Python environment that closely resembles the final implementation on the microcontroller.

Following the implementation of the MFCCs feature extraction, our attention shifted to model design. Here, we designed and trained an RNN model based on the LSTM layer with TensorFlow to classify music genres.

Once the model was trained, we...

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