Summary

The recipes presented in this chapter demonstrated how to build an end-to-end KWS application with Edge Impulse and the Arduino Nano.

Initially, we learned how to prepare the dataset by recording audio samples with a smartphone and the Arduino Nano directly from Edge Impulse.

Afterward, we delved into model design. Here, we introduced the MFE (or Mel-spectrogram) as a suitable input feature for training a CNN model for KWS.

Then, we trained a generic CNN and used the Edge Impulse EON Tuner to discover more efficient model architectures for our target platform regarding accuracy, latency performance, and memory consumption.

Finally, we tested the model’s accuracy on the test dataset and live audio samples recorded with a smartphone and deployed the KWS application on the Arduino Nano.

In this chapter, we have started discussing how to build a tinyML application with a microphone using Edge Impulse and the Arduino Nano. With the next project, we will...