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Measurement of the Directional Information Flow in fNIRS-Hyperscanning Data using the Partial Wavelet Transform Coherence Method
Published on: September 3, 2021
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Uncertainty quantification for direction-of-arrival estimation with conformal prediction
Ishan D Khurjekar1, Peter Gerstoft1
1Scripps Institute of Oceanography, University of California San Diego, La Jolla, California 92093, USA.
The Journal of the Acoustical Society of America
|August 15, 2023
Summary
Uncertainty quantification using conformal prediction provides rigorous confidence intervals for deep learning-based acoustic estimation. This enhances the real-world applicability of direction-of-arrival estimation systems.
Area of Science:
- Acoustics
- Machine Learning
- Signal Processing
Background:
- Deep learning (DL) methods are increasingly used for acoustic estimation tasks.
- Establishing confidence in DL predictions is crucial for real-world deployment.
- Uncertainty quantification (UQ) is essential for assessing the reliability of these systems.
Purpose of the Study:
- To propose and evaluate conformal prediction (CP) for UQ in deep learning-based direction-of-arrival (DOA) estimation.
- To demonstrate the effectiveness of CP in providing statistically rigorous confidence intervals.
- To enhance the practical applicability of DL for acoustic sensing.
Main Methods:
- Conformal prediction (CP) was applied for UQ in DOA estimation.
- CP computes confidence intervals using quantiles of user-defined scores.
- The method was tested with various DL models under environmental uncertainty.
Main Results:
- The proposed CP approach provides statistically sound confidence intervals for DOA estimation.
- CP enhances the reliability of DL-based acoustic estimation in uncertain environments.
- The method is adaptable to different DL architectures and score functions.
Conclusions:
- Conformal prediction offers a robust framework for UQ in DL-based acoustic estimation.
- This technique improves the trustworthiness and real-time applicability of DOA estimation systems.
- The study highlights the potential of CP for advancing acoustic sensing technologies.
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