Author

Axel G. Ekström

KTH Royal Institute of TechnologyORCID 0000-0002-6739-0838Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Bioengineering· 1 Scientific Communication & Education· 1

Signature Assays, Organisms & Methods

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Approach:deep learning· 1 Research Focus:software / tool· 1

Frequent Co-Authors

Runhui Song· 2 shared Jens EdlundiD· 1 shared Johan SjonsiD· 1 shared Jonas BeskowiD· 1 shared Kecheng ZhangiD· 1 shared Mechtild Tronnier· 1 shared Rui Tu· 1 shared

Activity by Year

2025-2026 · peak 1/yr

Co-Author Collaboration Network

7 top collaborators · 16 mutual co-author links

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2 shared papersBioengineering

Runhui Song

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2 papers

Recent·Most cited

Neural network-assisted analysis of tube vocal tract models

Runhui Song, Johan Sjons, Axel G. Ekström

The paper presents a pipeline using deep neural networks to analyze tube vocal tract models and explore articulatory-acoustic relationships through regression and machine learning.

Language Resources and Evaluation Conference (LREC) · Apr 30, 2026Peer-reviewedBioengineering

Open source software for tube vocal tract modeling, resonance prediction, illustration, and 3D printing

Runhui Song, Jonas Beskow, Jens Edlund, Mechtild Tronnier, Rui Tu, Kecheng Zhang, Axel G. Ekström

The paper introduces open-source software for tube vocal tract modeling, resonance prediction, and vowel synthesis without requiring coding.

bioRxiv · Oct 15, 2025 · PreprintScientific Communication & Education