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...