Recent years have witnessed significant advancements in deep learning architectures for music, along with the availability of more powerful embedded computing platforms specific to low-latency audio processing tasks. These recent developments have opened promising avenues for new Smart Musical Instruments and audio devices that rely on the execution of deep learning models on small embedded computers. Despite these new opportunities, there is a lack of instructions on how to deploy neural networks to many promising embedded audio platforms, including the embedded real-time Elk Audio OS. In this paper, we introduce a procedure for deploying audio deep learning models on embedded systems utilizing the Elk Audio OS. The procedure covers the entire process, from creating a compatible code project to executing and diagnosing it on a Raspberry Pi. Moreover, we discuss different approaches for the real-time execution of deep learning inference on embedded devices and provide alternatives for handling larger neural network models. To facilitate implementation and support future updates, we provide an online repository with a detailed guide, code templates, functional examples, and precompiled library binaries for the TensorFlow Lite and ONNX Runtime inference engines. This work aims to bridge the gap between deep learning model development and real-world deployment on embedded systems, fostering the development of self-contained digital musical instruments and other audio devices equipped with real-time deep learning capabilities. By promoting the deployment of neural networks to embedded devices, we contribute to the development of Smart Musical Instruments that are capable of providing musicians and audiences with unprecedented services.

Real-Time Embedded Deep Learning on Elk Audio OS / Stefani, Domenico; Turchet, Luca. - (2023), pp. 1-10. (Intervento presentato al convegno IS2 tenutosi a Pisa nel 26th -27th October 2023) [10.1109/IEEECONF59510.2023.10335204].

Real-Time Embedded Deep Learning on Elk Audio OS

Stefani, Domenico
Primo
;
Turchet, Luca
Secondo
2023-01-01

Abstract

Recent years have witnessed significant advancements in deep learning architectures for music, along with the availability of more powerful embedded computing platforms specific to low-latency audio processing tasks. These recent developments have opened promising avenues for new Smart Musical Instruments and audio devices that rely on the execution of deep learning models on small embedded computers. Despite these new opportunities, there is a lack of instructions on how to deploy neural networks to many promising embedded audio platforms, including the embedded real-time Elk Audio OS. In this paper, we introduce a procedure for deploying audio deep learning models on embedded systems utilizing the Elk Audio OS. The procedure covers the entire process, from creating a compatible code project to executing and diagnosing it on a Raspberry Pi. Moreover, we discuss different approaches for the real-time execution of deep learning inference on embedded devices and provide alternatives for handling larger neural network models. To facilitate implementation and support future updates, we provide an online repository with a detailed guide, code templates, functional examples, and precompiled library binaries for the TensorFlow Lite and ONNX Runtime inference engines. This work aims to bridge the gap between deep learning model development and real-world deployment on embedded systems, fostering the development of self-contained digital musical instruments and other audio devices equipped with real-time deep learning capabilities. By promoting the deployment of neural networks to embedded devices, we contribute to the development of Smart Musical Instruments that are capable of providing musicians and audiences with unprecedented services.
2023
4th International Symposium on the Internet of Sounds
Piscataway, NJ USA
IEEE
979-8-3503-8254-9
Stefani, Domenico; Turchet, Luca
Real-Time Embedded Deep Learning on Elk Audio OS / Stefani, Domenico; Turchet, Luca. - (2023), pp. 1-10. (Intervento presentato al convegno IS2 tenutosi a Pisa nel 26th -27th October 2023) [10.1109/IEEECONF59510.2023.10335204].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11572/400032
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