2026
Matthew Peachey
Investigating Continuous Interactions for generative Tools in the Context of Musical Creativity PhD Thesis
Dalhousie University, 2026.
Abstract | BibTeX | Tags: continuous interaction, continuous-interaction-and-machine-learning, control mapping, creativity support tools, digital musical instruments, machine learning, music, sound & music computing
@phdthesis{Peachey2026b,
title = {Investigating Continuous Interactions for generative Tools in the Context of Musical Creativity},
author = {Matthew Peachey},
year = {2026},
date = {2026-10-19},
urldate = {2026-10-19},
school = {Dalhousie University},
abstract = {This thesis investigates continuous interaction as a design principle for Artificial Intelligence (AI)-powered music creativity support tools. Discrete text prompting, the de facto standard for current generative AI systems, can limit musicians’ sense of artistic agency and creative ownership. Continuous interaction instead can enable fine-grained, real-time control over the generative process, helping musicians retain a sense of authorship over the generated output.
This thesis presents three studies that together examine how continuous interaction, integrated into real-time interfaces, can better support musicians’ creative workflows. First, I derived low-dimensional latent representations from a Variational Autoencoder with an intentionally small latent space, integrated them into a real-time GUI, and evaluated them in a mixed-methods study with both novice and experienced synthesizer users (n = 18). Second, I interviewed experienced musicians (n = 10) about their hesitations toward generative AI; their concerns centered on a lack of compelling tools and workflows, further motivating continuous interaction as a design preference. Finally, I developed a framework for Aging Digital Musical Instruments (AgingDMIs), a novel class of instruments that adapt to a user’s play style over time. I then implemented a hardware prototype instantiating one point in the AgingDMI design space, and evaluated it longitudinally with experienced musicians (n= 4) for twelve recurring sessions over several weeks.
The findings presented in this thesis suggest that integrating continuous interaction into generative AI-powered creativity support tools increases musicians’ artistic agency and creative ownership and fits better within their existing creative workflows. I further contribute a validated
design framework for AgingDMIs, a novel class of adaptive and personalized digital musical instruments, and a hardware prototype that together demonstrate the principles of that framework in practice.},
keywords = {continuous interaction, continuous-interaction-and-machine-learning, control mapping, creativity support tools, digital musical instruments, machine learning, music, sound & music computing},
pubstate = {published},
tppubtype = {phdthesis}
}
This thesis presents three studies that together examine how continuous interaction, integrated into real-time interfaces, can better support musicians’ creative workflows. First, I derived low-dimensional latent representations from a Variational Autoencoder with an intentionally small latent space, integrated them into a real-time GUI, and evaluated them in a mixed-methods study with both novice and experienced synthesizer users (n = 18). Second, I interviewed experienced musicians (n = 10) about their hesitations toward generative AI; their concerns centered on a lack of compelling tools and workflows, further motivating continuous interaction as a design preference. Finally, I developed a framework for Aging Digital Musical Instruments (AgingDMIs), a novel class of instruments that adapt to a user’s play style over time. I then implemented a hardware prototype instantiating one point in the AgingDMI design space, and evaluated it longitudinally with experienced musicians (n= 4) for twelve recurring sessions over several weeks.
The findings presented in this thesis suggest that integrating continuous interaction into generative AI-powered creativity support tools increases musicians’ artistic agency and creative ownership and fits better within their existing creative workflows. I further contribute a validated
design framework for AgingDMIs, a novel class of adaptive and personalized digital musical instruments, and a hardware prototype that together demonstrate the principles of that framework in practice.
Matthew Peachey; Joseph Malloch
Exploring Musical Creativity in the Era of Generative Artificial Intelligence: A Qualitative Inquiry Proceedings Article Forthcoming
In: Proceedings of the Conference on AI Music Creativity (AIMC), Berlin, Germany, Forthcoming.
BibTeX | Tags: creativity support tools, machine learning, music, qualitative inquiry
@inproceedings{Peachey2026,
title = {Exploring Musical Creativity in the Era of Generative Artificial Intelligence: A Qualitative Inquiry},
author = {Matthew Peachey and Joseph Malloch},
year = {2026},
date = {2026-09-16},
urldate = {2026-09-16},
booktitle = {Proceedings of the Conference on AI Music Creativity (AIMC)},
address = {Berlin, Germany},
keywords = {creativity support tools, machine learning, music, qualitative inquiry},
pubstate = {forthcoming},
tppubtype = {inproceedings}
}
Melika Besharati Amirkandeh; Derek Reilly
A Comparative Evaluation of Natural Language and Dashboard Visualization Interfaces for Data Monitoring Tasks Best Paper Proceedings Article
In: Proceedings of Graphics Interface 2026 (GI 2026), Canadian Human Computer Communications Society (CHCCS/SCDHM) ACM (ICPS), 2026.
BibTeX | Tags: chat interface, dashboard, machine learning, visualization
@inproceedings{nokey,
title = { A Comparative Evaluation of Natural Language and Dashboard Visualization Interfaces for Data Monitoring Tasks },
author = {Melika Besharati Amirkandeh and Derek Reilly},
year = {2026},
date = {2026-06-09},
urldate = {2026-06-09},
booktitle = {Proceedings of Graphics Interface 2026 (GI 2026)},
publisher = {ACM (ICPS)},
organization = {Canadian Human Computer Communications Society (CHCCS/SCDHM)},
keywords = {chat interface, dashboard, machine learning, visualization},
pubstate = {published},
tppubtype = {inproceedings}
}
Rowland Goddy-Worlu; James Forren; Claire Nicholas; Martha Dais Ferreira; Derek Reilly
Preserving Intangible Cultural Heritage through Augmented Reality and Machine Learning: A Case Study of the One-bead Netting Stitch Journal Article
In: ACM Journal on Computing and Cultural Heritage, 2026.
BibTeX | Tags: architecture, augmented reality, craft, cultural heritage, gesture, immersive visualization, machine learning, mixed reality, natural user interface, training
@article{Goddy-Worlu2026,
title = {Preserving Intangible Cultural Heritage through Augmented Reality and Machine Learning: A Case Study of the One-bead Netting Stitch},
author = {Rowland Goddy-Worlu and James Forren and Claire Nicholas and Martha Dais Ferreira and Derek Reilly},
year = {2026},
date = {2026-05-31},
urldate = {2026-05-31},
journal = {ACM Journal on Computing and Cultural Heritage},
keywords = {architecture, augmented reality, craft, cultural heritage, gesture, immersive visualization, machine learning, mixed reality, natural user interface, training},
pubstate = {published},
tppubtype = {article}
}
2025
Matthew Peachey; Sageev Oore; Joseph Malloch
Evaluating Low-Dimensional Latent Representations as a Creative Interface for Digital Synthesizers Proceedings Article
In: The Conference on AI Music Creativity (AIMC), 2025.
Links | BibTeX | Tags: continuous interaction, continuous-interaction-and-machine-learning, machine learning, music
@inproceedings{Peachey2025,
title = {Evaluating Low-Dimensional Latent Representations as a Creative Interface for Digital Synthesizers},
author = {Matthew Peachey and Sageev Oore and Joseph Malloch},
url = {https://zenodo.org/records/16946459},
year = {2025},
date = {2025-09-10},
urldate = {2025-09-10},
booktitle = {The Conference on AI Music Creativity (AIMC)},
keywords = {continuous interaction, continuous-interaction-and-machine-learning, machine learning, music},
pubstate = {published},
tppubtype = {inproceedings}
}
2024
Matthew Peachey; Karansingh Chauhan; Juliano Franz; Sue Molloy; Christopher Whidden
A Machine Learning Approach to Modeling Small Vessel Performance Proceedings Article
In: International OCEANS24 Conference, 2024.
Abstract | BibTeX | Tags: machine learning
@inproceedings{peachey2024machine,
title = {A Machine Learning Approach to Modeling Small Vessel Performance},
author = {Matthew Peachey and Karansingh Chauhan and Juliano Franz and Sue Molloy and Christopher Whidden},
year = {2024},
date = {2024-09-26},
urldate = {2024-09-26},
publisher = {International OCEANS24 Conference},
abstract = {The maritime industry is a critical component of global trade. However, it is also responsible for a significant amount of Greenhouse Gas emissions. While small vessels are often overlooked as main contributors to emissions, when analyzed as a group, they collectively have a meaningful impact. In order to help promote more sustainable operation of these vessels, it is important to to model fuel consumption based on different weather and sea conditions. To this end, we present a machine learning approach to modelling fuel consumption aboard small vessels using an LSTM architecture. In this paper, we discuss the process of collecting data aboard one such small vessel, the end-to-end model training & evaluation process, and discuss plans for future work.
},
keywords = {machine learning},
pubstate = {published},
tppubtype = {inproceedings}
}
Francisco Bravo; Joana Amorim; Melika Besharati; Peter Bodorik; Vitor Cerqueira; Nuno Gomes; Jennie Korus; Mariana Oliveira; João Pimentel; Derek Reilly; Tyler Sclodnick; Jon Grant; Ramón Filgueira; Christopher Whidden; Luis Torgo
Advancing Precision Aquaculture Through Big Data Analytics and Machine Learning in Canadian Fish Farming Proceedings Article
In: OCEANS 2024, IEEE 2024.
Links | BibTeX | Tags: geospatial analytics, machine learning, statistics, visualization
@inproceedings{Bravo2024,
title = {Advancing Precision Aquaculture Through Big Data Analytics and Machine Learning in Canadian Fish Farming},
author = {Francisco Bravo and Joana Amorim and Melika Besharati and Peter Bodorik and Vitor Cerqueira and Nuno Gomes and Jennie Korus and Mariana Oliveira and João Pimentel and Derek Reilly and Tyler Sclodnick and Jon Grant and Ramón Filgueira and Christopher Whidden and Luis Torgo},
url = {10.1109/OCEANS55160.2024.10754571},
doi = {https://doi.org/10.1109/OCEANS55160.2024.10754571},
year = {2024},
date = {2024-09-24},
urldate = {2024-09-24},
booktitle = {OCEANS 2024},
organization = {IEEE},
keywords = {geospatial analytics, machine learning, statistics, visualization},
pubstate = {published},
tppubtype = {inproceedings}
}
2023
Matthew Peachey; Sageev Oore; Joseph Malloch
Creating Latent Representations of Synthesizer Patches using Variational Autoencoders Proceedings Article
In: Proceedings of the 4th International Symposium on the Internet of Sounds (IS2), Pisa, Italy, 2023.
Links | BibTeX | Tags: continuous-interaction-and-machine-learning, machine learning, music
@inproceedings{Peachey2023,
title = {Creating Latent Representations of Synthesizer Patches using Variational Autoencoders},
author = {Matthew Peachey and Sageev Oore and Joseph Malloch},
url = {https://ieeexplore.ieee.org/document/10335466},
year = {2023},
date = {2023-10-26},
urldate = {2023-10-26},
booktitle = {Proceedings of the 4th International Symposium on the Internet of Sounds (IS2)},
address = {Pisa, Italy},
keywords = {continuous-interaction-and-machine-learning, machine learning, music},
pubstate = {published},
tppubtype = {inproceedings}
}
2022
Rowland Goddy-Worlu; Martha Dais Ferreira; Matthew Peachey; Claire Nicholas; James Forren; Derek Reilly
Capture and Recognition of Bead Weaving Activities using Hand Skeletal Data and an LSTM Deep Neural Network Proceedings Article
In: Proceedings of the 5th IEEE International Conference on Artificial Intelligence & Virtual Reality (AIVR 2022), IEEE, 2022.
Links | BibTeX | Tags: augmented reality, craft, creativity support tools, cultural heritage, dynamic guides, gesture, machine learning
@inproceedings{GoddyWorlu2022,
title = {Capture and Recognition of Bead Weaving Activities using Hand Skeletal Data and an LSTM Deep Neural Network},
author = {Rowland Goddy-Worlu and Martha Dais Ferreira and Matthew Peachey and Claire Nicholas and James Forren and Derek Reilly},
url = {https://doi.org/10.1109/AIVR56993.2022.00025},
doi = {10.1109/AIVR56993.2022.00025},
year = {2022},
date = {2022-12-12},
urldate = {2022-12-12},
booktitle = {Proceedings of the 5th IEEE International Conference on Artificial Intelligence & Virtual Reality (AIVR 2022)},
publisher = {IEEE},
keywords = {augmented reality, craft, creativity support tools, cultural heritage, dynamic guides, gesture, machine learning},
pubstate = {published},
tppubtype = {inproceedings}
}