Exploring the current landscape of open research software in room acoustics

Over the span of six decades, researchers have dedicated considerable efforts towards the development of computational methodologies for predicting room acoustics. These methodologies comprise the calculation of detailed impulse responses, frequency responses, and the prediction of quantities and indicators. The classification of these computational approaches continues to evolve, particularly with the recent integration of machine learning methods into the realm of room acoustics. Notably, the research software or code underpinning these advancements is not as readily accessible to the public as the results it has generated. This lack of accessibility has posed challenges to reproducibility and potentially impacted the overall quality of scientific endeavors as well as hampered collaboration, hindering the progress within the field. This study provides insights into the current state of open room acoustics research software, presenting quantitative developments over the years. It further explores the measurable impact of shared research software and offers an outlook on the prospective value of open research software in room acoustics.

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Publication Details

Journal
NOISE-CON proceedings
Published
2024-10-04
DOI
https://doi.org/10.3397/in_2024_3656
Citations
2
Primary Topic
Context-Aware Activity Recognition Systems
Type
article
Field-Weighted Citation Impact
0.50
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article

Exploring the current landscape of open research software in room acoustics

Maarten Hornikx, Lavínia Paganini, Alexander Serebrenik, Huiqing Wang et al.
2 citations
NOISE-CON proceedings
Context-Aware Activity Recognition Systems
0.50
article

Exploring the current landscape of open research software in room acoustics

Maarten Hornikx, Lavínia Paganini, Alexander Serebrenik, Huiqing Wang, Alexander Nolte, Ilaria Fichera
article en
2 citations

Abstract

Over the span of six decades, researchers have dedicated considerable efforts towards the development of computational methodologies for predicting room acoustics. These methodologies comprise the calculation of detailed impulse responses, frequency responses, and the prediction of quantities and indicators. The classification of these computational approaches continues to evolve, particularly with the recent integration of machine learning methods into the realm of room acoustics. Notably, the research software or code underpinning these advancements is not as readily accessible to the public as the results it has generated. This lack of accessibility has posed challenges to reproducibility and potentially impacted the overall quality of scientific endeavors as well as hampered collaboration, hindering the progress within the field. This study provides insights into the current state of open room acoustics research software, presenting quantitative developments over the years. It further explores the measurable impact of shared research software and offers an outlook on the prospective value of open research software in room acoustics.

NOISE-CON proceedingsVol. 270(6)
Eindhoven University of Technology (NL)
Industry, innovation and infrastructure
Openalex Percentile: Top 34%
Context-Aware Activity Recognition Systems
0.50
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Exploring the current landscape of open research software in room acoustics — Maarten Hornikx, Lavínia Paganini, et al. · NOISE-CON proceedings (2024) | TGRS Research Map | TGRS