# Pipeline for recording datasets and running neural networks on the Bela embedded hardware platform

arXiv: https://arxiv.org/abs/2306.11389 · HTML: https://mlsanity.com/paper/2306.11389/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Jun 2023.

## Built on by (most cited first, 1 of 1)

- [ANIRA: An Architecture for Neural Network Inference in Real-Time Audio Applications](https://mlsanity.com/paper/2506.12665/) (2025)

## Abstract (arXiv)

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.