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

Designing and training an LSTM RNN model

In this project, the model designed for classifying music genres is an LSTM RNN, as illustrated in the following diagram:

Figure 6.9: LSTM recurrent neural network for music genre classification

As shown in the previous image, the MFCCs extracted from 1 second of raw audio are the input for the model, which consists of the following layers:

2 x LSTM layers with 32 number of units each (Num. units)
1 x Dropout layer with a 50% rate (0.5)
1 x Fully connected layer with three output neurons, followed by a Softmax activation function

In this recipe, we will design and train this LSTM model with TensorFlow.

Getting ready

In Chapter 4, Using Edge Impulse and the Arduino Nano to Control LEDs with Voice Commands, we addressed an audio classification problem using a standard convolutional neural network (CNN) that learned visual patterns from the Mel-filterbank energy...