Room-acoustic simulations as an alternative to measurements for audio-algorithm evaluation
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arXiv
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| Autori principali: | , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866915705980452864 |
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| author | Götz, Georg Nielsen, Daniel Gert Guðjónsson, Steinar Pind, Finnur |
| author_facet | Götz, Georg Nielsen, Daniel Gert Guðjónsson, Steinar Pind, Finnur |
| contents | Audio-signal-processing and audio-machine-learning (ASP/AML) algorithms are ubiquitous in modern technology like smart devices, wearables, and entertainment systems. Development of such algorithms and models typically involves a formal evaluation to demonstrate their effectiveness and progress beyond the state-of-the-art. Ideally, a thorough evaluation should cover many diverse application scenarios and room-acoustic conditions. However, in practice, evaluation datasets are often limited in size and diversity because they rely on costly and time-consuming measurements. This paper explores how room-acoustic simulations can be used for evaluating ASP/AML algorithms. To this end, we evaluate three ASP/AML algorithms with room-acoustic measurements and data from different simulation engines, and assess the match between the evaluation results obtained from measurements and simulations. The presented investigation compares a numerical wave-based solver with two geometrical acoustics simulators. While numerical wave-based simulations yielded similar evaluation results as measurements for all three evaluated ASP/AML algorithms, geometrical acoustic simulations could not replicate the measured evaluation results as reliably. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_05175 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Room-acoustic simulations as an alternative to measurements for audio-algorithm evaluation Götz, Georg Nielsen, Daniel Gert Guðjónsson, Steinar Pind, Finnur Audio and Speech Processing Machine Learning Audio-signal-processing and audio-machine-learning (ASP/AML) algorithms are ubiquitous in modern technology like smart devices, wearables, and entertainment systems. Development of such algorithms and models typically involves a formal evaluation to demonstrate their effectiveness and progress beyond the state-of-the-art. Ideally, a thorough evaluation should cover many diverse application scenarios and room-acoustic conditions. However, in practice, evaluation datasets are often limited in size and diversity because they rely on costly and time-consuming measurements. This paper explores how room-acoustic simulations can be used for evaluating ASP/AML algorithms. To this end, we evaluate three ASP/AML algorithms with room-acoustic measurements and data from different simulation engines, and assess the match between the evaluation results obtained from measurements and simulations. The presented investigation compares a numerical wave-based solver with two geometrical acoustics simulators. While numerical wave-based simulations yielded similar evaluation results as measurements for all three evaluated ASP/AML algorithms, geometrical acoustic simulations could not replicate the measured evaluation results as reliably. |
| title | Room-acoustic simulations as an alternative to measurements for audio-algorithm evaluation |
| topic | Audio and Speech Processing Machine Learning |
| url | https://arxiv.org/abs/2509.05175 |