Kernel Learning for Sound Field Estimation with L1 and L2 Regularizations

Horiuchi, Ryosuke; Koyama, Shoichi; Ribeiro, Juliano G. C.; Ueno, Naohiro; Saruwatari, Hiroshi

doi:10.1109/waspaa52581.2021.9632731

Cited by 12 publications

(2 citation statements)

References 17 publications

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“…Even when the primary noise source direction is unknown, we can also apply the kernel function in (9) by setting β to 0. Furthermore, when multiple primary noise sources exist, it is possible to define the kernel function by the weighted sum of (9) with different β and η values, and optimize the parameters from the measured signals [21].…”

Section: Kernel Interpolation Of Primary Noise Field From Reference M...mentioning

confidence: 99%

Spatial Active Noise Control Method Based On Sound Field Interpolation From Reference Microphone Signals

Arikawa¹,

Koyama²,

Saruwatari³

2023

Preprint

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A spatial active noise control (ANC) method based on the interpolation of a sound field from reference microphone signals is proposed. In most current spatial ANC methods, a sufficient number of error microphones are required to reduce noise over the target region because the sound field is estimated from error microphone signals. However, in practical applications, it is preferable that the number of error microphones is as small as possible to keep a space in the target region for ANC users. We propose to interpolate the sound field using reference microphones, which are normally placed outside the target region, instead of the error microphones. We derive a fixed filter for spatial noise reduction on the basis of the kernel ridge regression for sound field interpolation. Furthermore, to compensate for estimation errors, we combine the proposed fixed filter with multichannel ANC based on a transition of the control filter using the error microphone signals. Numerical experimental results indicate that regional noise can be sufficiently reduced by the proposed methods even when the number of error microphones is particularly small.

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Section: Kernel Interpolation Of Primary Noise Field From Reference M...mentioning

confidence: 99%

Spatial Active Noise Control Method Based On Sound Field Interpolation From Reference Microphone Signals

Arikawa¹,

Koyama²,

Saruwatari³

2023

Preprint

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show abstract

“…Although the profile of the reverberant component is understood, the exact balance between the direct and lateral component gains is not known outright. There are several methods of hyperparameter optimization used for kernel ridge regression [24,25]. For this application, we employ leave-one-out cross-validation (LOO) because of its simplicity and nearly unbiased nature [26].…”

Section: Hyperparameter Optimization For Directional Weightmentioning

confidence: 99%

Region-to-region kernel interpolation of acoustic transfer function with directional weighting

Ribeiro,

Koyama,

Saruwatari

2022

Preprint

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A method of interpolating the acoustic transfer function (ATF) between regions that takes into account both the physical properties of the ATF and the directionality of region configurations is proposed. Most spatial ATF interpolation methods are limited to estimation in the region of receivers. A kernel method for region-to-region ATF interpolation makes it possible to estimate the ATFs for both source and receiver regions from a discrete set of ATF measurements. We newly formulate the reproducing kernel Hilbert space and associated kernel function incorporating directional weight to enhance the interpolation accuracy. We also investigate hyperparameter optimization methods for this kernel function. Numerical experiments indicate that the proposed method outperforms the method without the use of directional weighting.

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Multi-Stage Feature Decorrelation Constraints for Improving CNN Classification Performance

Zhu,

Wang,

et al. 2023

2023 China Automation Congress (CAC)

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Kernel Learning for Sound Field Estimation with L1 and L2 Regularizations

Cited by 12 publications

References 17 publications

Spatial Active Noise Control Method Based On Sound Field Interpolation From Reference Microphone Signals

Spatial Active Noise Control Method Based On Sound Field Interpolation From Reference Microphone Signals

Region-to-region kernel interpolation of acoustic transfer function with directional weighting

Multi-Stage Feature Decorrelation Constraints for Improving CNN Classification Performance

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