Pipeline for recording datasets and running neural networks on the Bela embedded hardware platform
Abstract
Deploying deep learning models on embedded devices is an arduous task: oftentimes, there exist no platform-specific instructions, and compilation times can be considerably large due to the limited computational resources available on-device. Moreover, many music-making applications demand real-time inference. Embedded hardware platforms for audio, such as Bela, offer an entry point for beginners into physical audio computing; however, the need for cross-compilation environments and low-level software development tools for deploying embedded deep learning models imposes high entry barriers on non-expert users. We present a pipeline for deploying neural networks in the Bela embedded hardware platform. In our pipeline, we include a tool to record a multichannel dataset of sensor signals. Additionally, we provide a dockerised cross-compilation environment for faster compilation. With this pipeline, we aim to provide a template for programmers and makers to prototype and experiment with neural networks for real-time embedded musical applications.
Keywords
Cite
@article{arxiv.2306.11389,
title = {Pipeline for recording datasets and running neural networks on the Bela embedded hardware platform},
author = {Teresa Pelinski and Rodrigo Diaz and Adán L. Benito Temprano and Andrew McPherson},
journal= {arXiv preprint arXiv:2306.11389},
year = {2023}
}