3D sound source localization based on coherence-adjusted monopole dictionary and modified convex clustering

Tachikawa, Tomoya; Yatabe, Kohei; Oikawa, Yasuhiro

doi:10.1016/j.apacoust.2018.04.033

Cited by 10 publications

(5 citation statements)

References 52 publications

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“…According to the distance between the sound source and the microphone array, the sound propagation model is divided into the near-field model and the far-field model. 45,46 For the near-field model, sound waves are transmitted in the form of spherical sound waves. The general near-field judgment formula iswhere D is the array aperture, λ is the signal wavelength, and L is the distance between the sound source and the microphone array.…”

Section: Signal Model and 3d-music Algorithmmentioning

confidence: 99%

A fast 3D-MUSIC method for near-field sound source localization based on the bat algorithm

Yang

Sun

Guo

et al. 2022

International Journal of Aeroacoustics

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To improve the computation and real-time performances of the multiple signal classification (MUSIC) algorithm in 3D space, a fast sound source localization method based on the bat algorithm (BA) and the 3D-MUSIC, called BA-based 3D-MUSIC algorithm (3D-BMUSIC), is presented in this paper. 3D-BMUSIC greatly reduces the computation load by replacing the regular grid search with the BA. First, the near-field spherical wave model is established to obtain the spectral function of the 3D-MUSIC. Then, the spectral function is defined as the fitness function, which calculates the fitness value corresponding to each bat position. Finally, the global optimal bat position with the largest fitness value, as sound source localization, is obtained by successive iteration and sorting. The simulation and experiment show that 3D-BMUSIC accurately estimates the DOA and distance of near-field sources, and the root-mean-square error (RMSE) of 3D-BMUSIC is less than that of 3D-MUSIC. In addition, 3D-BMUSIC effectively reduces the computation time by approximately 96–98%. With shorter computation time and higher efficiency, 3D-BMUSIC promotes hardware implementation and is more suitable for high-precision localization of near-field sound sources.

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Section: Signal Model and 3d-music Algorithmmentioning

confidence: 99%

A fast 3D-MUSIC method for near-field sound source localization based on the bat algorithm

Yang

Sun

Guo

et al. 2022

International Journal of Aeroacoustics

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“…In the SH-domain FxLMS algorithm, the reference SH coefficients x are used to generate the SH coefficients of the antinoise noise field as z = gxw, as well as to update the adaptive filter w using (20). Therefore, the precise estimation of the reference SH coefficients is important for both the noise attenuation and the filter convergence performance.…”

Section: B Spherical-harmonic-domain Adaptive Filter Updatementioning

confidence: 99%

“…underlying sound field is due to a small number of underlying sound sources. In this context, compressive sensing (CS) has been successfully applied to sound field reproduction, sound field separation, and sound localization [16]- [20]. Koyama and co-workers [16], [17] showed that the spatial aliasing [21] originating from spatial sampling can be avoided in sound field reproduction.…”

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.

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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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“…In TACHIKAWA, T. et al work [8] it's proposed a system that can locate the angulation of the sound location, as well as the distance to the microphones used by the system. The method Direction-of-arrival (DOA) is used to locate the direction of sound, but since there is a problem with the continuity of system values, a grid of available space was used to prevent DOA system initialization errors.…”

Section: Sound Source Localization and Trackingmentioning

confidence: 99%

“…The increase in the number of points according to the angle at the adjacent points decreases, is exponential when comparing the 3D environment to the 2D, so the use of a 2D environment reduces the complexity of location analysis of the sound emitter in question. The use of the system for 3D localization of sound emitters is possible and uses a dictionary of 3D points and a modified convex clustering for localization [8]. MURRAY, J. C. et al It is seen that the solution in [9] was effective, and the hybrid model described had an effective result in locating and tracking sound sources, with accuracy of 1.5 degrees around center point 0, but around the range of -90 degrees to +90 degree the system presented accuracy of 7.5 degrees.…”

Section: Sound Source Localization and Trackingmentioning

confidence: 99%

Analysis of sound source localization and tracking for the @home service robot in multiple distances

Meyer¹,

Aquino-Junior²

2020

Proceedings of the III Brazilian Humanoid Robot Workshop (BRAHUR) and IV Brazilian Workshop on Service Robotics (BRASERO)

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Robots have been used in several areas, and in many of them it is common to see an interaction between a human and the robot. Human-Robot Interaction (HRI) became a crucial part on the process of understanding and creating machines, which will communicate using voice as the main factor. Speech is used as an input and an output of the communication between machines and humans, in the way that commands and answers are performed using this method. There are several Sound Source Localization and Tracking (SSLT) methods that may be used in an ambiance and many systems can be used to complete this task. To succeed in the usage of this knowledge source, establishing an appropriate system, which allows doing the calculus and implementation in a service robot, is necessary and, using the past work of the author, it was concluded. The main objective of this paper is to introduce some of the available methods for SSLT in the literature, and present results of the implementation and implantation of methods for a Domestic Service Robot, with the objective of having a more natural interaction with humans, allowing to interact the human facing front, instead of facing any direction.

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3D sound source localization based on coherence-adjusted monopole dictionary and modified convex clustering

Cited by 10 publications

References 52 publications

A fast 3D-MUSIC method for near-field sound source localization based on the bat algorithm

A fast 3D-MUSIC method for near-field sound source localization based on the bat algorithm

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

Analysis of sound source localization and tracking for the @home service robot in multiple distances

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