tinyML Talks on February 13, 2024 “Unpacking the music genre recognition project from the TinyML Cookbook, second edition!” by Gian Marco Iodice

We held our next tinyML Talks webcast. Gian Marco Iodice from Arm presented Unpacking the music genre recognition project from the TinyML Cookbook, second edition! on February 13, 2024.

In this exclusive practice session, Gian Marco Iodice will demonstrate how to build a music genre recognition application on the Raspberry Pi Pico using TensorFlow Lite for Microcontrollers and the CMSIS-DSP library. This project is part of the TinyML Cookbook’s second edition, and it is proposed to demonstrate how the target device influences our design choice, from the feature extraction to the model design, when deploying ML models on microcontrollers.
The talk will start by tailoring the Mel-frequency cepstral coefficients (MFCCs) feature extraction for the Raspberry Pi Pico. Here, you will learn how fixed-point arithmetic can help minimize the latency performance and show how the CMSIS-DSP library provides tremendous support in employing this numerical format.
Afterward, Gian Marco will present the design choices for the ML model capable of recognizing music genres with a long-short-term memory (LSTM) recurrent neural network (RNN).
Finally, he will show how to deploy the final application on the Raspberry Pi Pico with the help of TensorFlow Lite for Microcontrollers.
A book giveaway will follow at the end of this presentation for the chance to get a free copy of the second edition of the TinyML Cookbook!

Gian Marco Iodice is an experienced edge and mobile computing specialist at Arm for Machine Learning (ML). He is also chair of the global meetups for the tinyML foundation since 2022. He received the MSc with honors in electronic engineering from the University of Pisa (Italy), where he specialized in HW/SW co-design for embedded systems. Within Arm, he leads the engineering developments for Generative AI and the Arm Compute Library, which he co-created in 2017 to run ML workloads on Arm processors as efficiently as possible. Arm Compute Library, designed to deliver the best performance across Arm Cortex-A CPUs and Mali GPUs, is deployed on billions of devices worldwide – from servers to smartphones. In 2023, he collaborated with the University of Cambridge to integrate ML functionalities on an Arm Cortex-M microcontroller powered by algae. This ground-breaking work was showcased at the tinyML EMEA Innovation Forum in Amsterdam in June of the same year. Still, in 2023, Gian Marco contributed to developing the EdTech for Good Curation Framework. This framework, developed by UNICEF in collaboration with Arm and the Asian Development Bank (ADB), represents a significant step forward in the responsible use of technology in education because it enables public entities and international organizations to evaluate digital educational technologies, prioritizing learning outcomes and children’s safety.

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Gian Marco Iodice

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