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Bayesian machine learning framework for time-domain prediction of multirotor vehicle noisea).

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Title: Bayesian machine learning framework for time-domain prediction of multirotor vehicle noisea).
Authors: Lee, Howon1 (AUTHOR) hlee981@gatech.edu, Ko, Jeongwoo2 (AUTHOR), Seshadri, Pranay1 (AUTHOR), Rauleder, Juergen1 (AUTHOR)
Source: Journal of the Acoustical Society of America. Apr2026, Vol. 159 Issue 4, p3418-3435. 18p.
Subjects: Gaussian processes, Aeroacoustics, Signal processing, Time, Quantitative research
Abstract: This work presents a Bayesian machine learning framework developed to predict aeroacoustic time-series signals generated by a quadrotor vehicle in forward flight at varying velocities. In this effort, a Gaussian process (GP) regression model is trained using a database of simulated signals produced by the Comprehensive Multi-rotor Noise Assessment framework. Unlike traditional frequency-domain models, the GP model directly predicts the time-domain signal, inherently capturing both amplitude and phase information of relevant frequency components. This capability is achieved by partitioning the tonal and broadband components during pre-processing, and capturing each component via a blade passage frequency-informed Fourier kernel and a Gaussian likelihood model, respectively. The resulting model is probabilistic in nature, inherently capturing the associated prediction uncertainty. Quantitative evaluations demonstrate strong agreement with ground truth signals in both time and frequency domains, with mean loudness errors of 1.11% in decibels and 5.55% in sones. The mean psychoacoustic annoyance error is found to be approximately 10%. The model is also computationally efficient compared to traditional physics-based solvers, requiring 0.1803 s to generate a time-series signal sampled at 44 100 Hz on a single NVIDIA A100 GPU (NVIDIA, Santa Clara, CA). [ABSTRACT FROM AUTHOR]
Copyright of Journal of the Acoustical Society of America is the property of American Institute of Physics and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
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DbLabel: Engineering Source
An: 193402379
AccessLevel: 6
PubType: Academic Journal
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  Data: Bayesian machine learning framework for time-domain prediction of multirotor vehicle noisea).
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  Data: <searchLink fieldCode="JN" term="%22Journal+of+the+Acoustical+Society+of+America%22">Journal of the Acoustical Society of America</searchLink>. Apr2026, Vol. 159 Issue 4, p3418-3435. 18p.
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  Data: <searchLink fieldCode="DE" term="%22Gaussian+processes%22">Gaussian processes</searchLink><br /><searchLink fieldCode="DE" term="%22Aeroacoustics%22">Aeroacoustics</searchLink><br /><searchLink fieldCode="DE" term="%22Signal+processing%22">Signal processing</searchLink><br /><searchLink fieldCode="DE" term="%22Time%22">Time</searchLink><br /><searchLink fieldCode="DE" term="%22Quantitative+research%22">Quantitative research</searchLink>
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  Label: Abstract
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  Data: This work presents a Bayesian machine learning framework developed to predict aeroacoustic time-series signals generated by a quadrotor vehicle in forward flight at varying velocities. In this effort, a Gaussian process (GP) regression model is trained using a database of simulated signals produced by the Comprehensive Multi-rotor Noise Assessment framework. Unlike traditional frequency-domain models, the GP model directly predicts the time-domain signal, inherently capturing both amplitude and phase information of relevant frequency components. This capability is achieved by partitioning the tonal and broadband components during pre-processing, and capturing each component via a blade passage frequency-informed Fourier kernel and a Gaussian likelihood model, respectively. The resulting model is probabilistic in nature, inherently capturing the associated prediction uncertainty. Quantitative evaluations demonstrate strong agreement with ground truth signals in both time and frequency domains, with mean loudness errors of 1.11% in decibels and 5.55% in sones. The mean psychoacoustic annoyance error is found to be approximately 10%. The model is also computationally efficient compared to traditional physics-based solvers, requiring 0.1803 s to generate a time-series signal sampled at 44 100 Hz on a single NVIDIA A100 GPU (NVIDIA, Santa Clara, CA). [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
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  Data: <i>Copyright of Journal of the Acoustical Society of America is the property of American Institute of Physics and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.1121/10.0043469
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      – Code: eng
        Text: English
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        PageCount: 18
        StartPage: 3418
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      – SubjectFull: Gaussian processes
        Type: general
      – SubjectFull: Aeroacoustics
        Type: general
      – SubjectFull: Signal processing
        Type: general
      – SubjectFull: Time
        Type: general
      – SubjectFull: Quantitative research
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      – TitleFull: Bayesian machine learning framework for time-domain prediction of multirotor vehicle noisea).
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            NameFull: Lee, Howon
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            NameFull: Ko, Jeongwoo
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            NameFull: Seshadri, Pranay
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            NameFull: Rauleder, Juergen
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            – D: 01
              M: 04
              Text: Apr2026
              Type: published
              Y: 2026
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