Teresa Pelinski
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Research Subjects
Teresa Pelinski has a total of 11 co-authors. Here are the top co-authors by number of shared publications:
- Adan L Benito Temprano
Centre for Digital Music, Queen Mary University of London, London, United Kingdom
2 shared publications - Andrew McPherson
Queen Mary University of London
1 shared publication - Andrew McPherson
Dyson School of Design Engineering, Imperial College London, London, United Kingdom
1 shared publication - Chris Kiefer
Experimental Music Technologies Lab, Department of Music, University of Sussex, Brighton, United Kingdom
1 shared publication - Franco Santiago Caspe
Centre for Digital Music Queen Mary University of London London UK
1 shared publication - Jack Armitage
Intelligent Instruments Lab Iceland University of the Arts Reykjavík Iceland
1 shared publication
Latest Publications
- Open Access
Pipeline for recording datasets and running neural networks on the Bela embedded hardware platform
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.
Open AccessPipeline for recording datasets and running neural networks on the Bela embedded hardware platform
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.