Spatial Noise-Field Control With Online Secondary Path Modeling: A Wave-Domain Approach

Zhang, Wen; Hofmann, Christian; Buerger, Michael E.; Abhayapala, Thushara D.; Kellermann, Walter

doi:10.1109/taslp.2018.2864577

Cited by 19 publications

(8 citation statements)

References 53 publications

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“…We write the matrix form of (25) as e = T e S e , where T e ∈ C (N +1) 2 ×L e is the SH transformation matrix. From (11) and (13), the SH coefficients of the secondary path can be derived in a matrix form as g act = T e G act Y,…”

Section: Actual Room Experimentsmentioning

confidence: 99%

“…Thus, we refer to this method as the multiple-point method. In contrast, harmonic-domain-based ANC methods, which decompose signals into harmonic basis functions, e.g., spherical harmonic (SH) functions, were proposed [7]- [11]. The advantages of this approach are that the sound field can be controlled in terms of a region rather than points and a fast convergence can be achieved by uniformly placing the microphones and secondary sources on an applicable surface, e.g., a sphere.…”

Section: Introductionmentioning

confidence: 99%

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Spherical-Harmonic-Domain Feedforward Active Noise Control Using Sparse Decomposition of Reference Signals from Distributed Sensor Arrays

Maeno

Mitsufuji

Samarasinghe

et al. 2020

IEEE/ACM Trans. Audio Speech Lang. Process.

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Active acoustic noise attenuation over a sizable space is a challenging problem in signal processing. The noise attenuation performance of feedforward active noise control (ANC) relies on the preciseness of a reference signal of a primary noise field. To capture the precise reference signal for controlling a sizable space, a large number of reference microphones are required, which reduces system viability. In this study, we exploit an efficient representation of the reference signal in spherical harmonic (SH) domain by utilizing the inherent sparseness of the noise field. The main contributions of this work are as follows. (1) A general reference microphone geometry can be used. The implementation difficulty in the array structure, which is recognized as the common issue of SH-domain signal processing, e.g., use of a fully surrounding spherical array, is reduced by using the fields translation based on the addition theorem. (2) The accuracy of low-frequency signal decomposition is improved. The low accuracy of low-frequency signal decomposition in compressive sensing (CS), which is commonly reported in the literature, is improved by applying signal representation in SH domain. (3) System robustness is increased. The robustness of the system is increased by considering a noise source spatial distribution of both the interior and exterior sound fields, which is not possible in the case of a general signal representation in SH domain. Experimental results indicate that the noise attenuation performance of our proposed method exceeds that of existing solutions. The flexibility of the array structure is also increased, which leads to a more feasible practical system setup.

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Section: Actual Room Experimentsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Spherical-Harmonic-Domain Feedforward Active Noise Control Using Sparse Decomposition of Reference Signals from Distributed Sensor Arrays

Maeno

Mitsufuji

Samarasinghe

et al. 2020

IEEE/ACM Trans. Audio Speech Lang. Process.

Self Cite

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show abstract

“…domain ANC is calculated on three stages, the number of F F T operations, wave domain transform, and wave domain processing. A wave domain method to cancel the tonal noises is presented in [7]. To calculate the computational complexity, suppose a wave domain ANC has P number of secondary sources, Q number of error sensors, and K number of modes.…”

Section: A Wave Domain Approachmentioning

confidence: 99%

“…However, in some applications it is unrealistic and ineffective to achieve the noise attenuation at the error microphone location. Therefore, to introduce a more flexible positioning of the "zone of quiet", several time domain [1]- [2] [3] [4], frequency domain [5], [6], and wave domain [7], [8] virtual sensing methods have been suggested in the literature.…”

Section: Introductionmentioning

confidence: 99%

On FxLMS Scheme for Active Noise Control at Remote Location

Munir

Abdulla

2020

IEEE Access

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The conventional adaptive noise control algorithms create a zone of quiet at the location of an error microphone. Several virtual active noise control (ANC) algorithms have been suggested in the literature to introduce a more flexible positioning of the zone of quiet. The main objective of the proposed ANC system in our research is to minimize the noise disturbance on human hearing by considering the auditory system characteristics. We have introduced the human perception of acoustic disturbances in our ANC modeling. This paper presents a novel approach based on a psychoacoustically enabled remote ANC algorithm to minimize the acoustic noise at a zone away from the error microphone location. This approach depends on modeling the transfer function between the error microphone and the remote zone of quiet. The developed model has been implemented using an FPGA real-time module, and the performance is validated at four different remote locations. Noise-weighting filters are incorporated to observe the psychoacoustic characteristics of the proposed model. In the experimental setup, the acoustic noise at the physical and remote locations are monitored to quantify the noise reduction characteristics of the proposed algorithm. The simulation and experimental results show a noise reduction of 15-18 dBs has been achieved, which is an average improvement of 5-8 dBs over the standard ANC model.

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“…In literature, both time-domain [7,8] and frequency-domain [9,10] algorithms have been implemented in multichannel ANC systems, which can cancel the noise at error sensor positions and their close surroundings [10]. Recently, ANC over space has been approached via Wave field synthesis (WFS)-based wave-domain algorithms [11][12][13] and (cylindrical/spherical) harmonic-based wave-domain algorithms [14][15][16][17][18][19], with which the noise over entire region of interest can be cancelled directly. Here onwards, we use the terminology 'wave-domain ANC' to refer to harmonics-based wave-domain ANC.…”

Section: Introductionmentioning

confidence: 99%

Active Noise Control over Space: A Subspace Method for Performance Analysis

et al. 2019

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In this paper, we investigate the maximum active noise control performance over a three-dimensional (3-D) spatial space, for a given set of secondary sources in a particular environment. We first formulate the spatial active noise control (ANC) problem in a 3-D room. Then we discuss a wave-domain least squares method by matching the secondary noise field to the primary noise field in the wave domain. Furthermore, we extract the subspace from wave-domain coefficients of the secondary paths and propose a subspace method by matching the secondary noise field to the projection of primary noise field in the subspace. Simulation results demonstrate the effectiveness of the proposed algorithms by comparison between the wave-domain least squares method and the subspace method, more specifically the energy of the loudspeaker driving signals, noise reduction inside the region, and residual noise field outside the region. We also investigate the ANC performance under different loudspeaker configurations and noise source positions.

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Spatial Noise-Field Control With Online Secondary Path Modeling: A Wave-Domain Approach

Cited by 19 publications

References 53 publications

Spherical-Harmonic-Domain Feedforward Active Noise Control Using Sparse Decomposition of Reference Signals from Distributed Sensor Arrays

Spherical-Harmonic-Domain Feedforward Active Noise Control Using Sparse Decomposition of Reference Signals from Distributed Sensor Arrays

On FxLMS Scheme for Active Noise Control at Remote Location

Active Noise Control over Space: A Subspace Method for Performance Analysis

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