• Smart Structures and Systems
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• v.29 no.6
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• pp.777-783
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• 2022

Even though several deep learning-based methods have been applied in the field of acoustic source localization, the previous works have only been conducted using the two-dimensional representation of the beamforming maps, particularly with the planar array system. While the acoustic sources are more required to be localized in a spherical microphone array system considering that we live and hear in the 3D world, the conventional 2D equirectangular map of the spherical beamforming map is highly vulnerable to the distortion that occurs when the 3D map is projected to the 2D space. In this study, a 3D deep learning approach is proposed to fulfill accurate source localization via distortion-free 3D representation. A target function is first proposed to obtain 3D source distribution maps that can represent multiple sources' positional and strength information. While the proposed target map expands the source localization task into a point-wise prediction task, a PointNet-based deep neural network is developed to precisely estimate the multiple sources' positions and strength information. While the proposed model's localization performance is evaluated, it is shown that the proposed method can achieve improved localization results from both quantitative and qualitative perspectives.

• The Journal of the Acoustical Society of Korea
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• v.39 no.1
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• pp.47-56
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• 2020

The sound source localization technique has various application fields in the era of internet-of-things, for which the probe size becomes critical. The localization methods using the acoustic intensity vector has an advantage of downsizing the layout of the array owing to a small finite-difference error for the short distance between adjacent microphones. In this paper, the acoustic intensity vector and the Time Difference of Arrival (TDoA) method are compared in the viewpoint of the localization error in the far-field. The comparison is made according to the change of spacing between adjacent microphones of the three-dimensional microphone array arranged in a tetrahedral shape. An additional test is conducted in the reverberant field by varying the reverberation time to verify the effectiveness of the methods applied to the actual environments. For estimating the TDoA, the Generalized Cross Correlation-Phase transform (GCC-PHAT) algorithm is adopted in the computation. It is found that the mean localization error of the acoustic intensimetry is 2.9° and that of the GCC-PHAT is 7.3° for T₆₀ = 0.4 s, while the error increases as 9.9°, 13.0° for T₆₀ = 1.0 s, respectively. The data supports that a compact array employing the acoustic intensimetry can localize of the sound source in the actual environment with the moderate reflection conditions.

• Proceedings of the IEEK Conference
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• 2000.07a
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• pp.264-267
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• 2000

This paper describes a real-time 3D sound localization algorithm to be implemented with the use of a Bow power embedded DSP. This algorithm first divides the audible frequency band into three, on the basis of the analysis of the sound reflection and diffraction effects through different media from a certain sound source to human ears, and then in each subband a specific procedure is devised fur the 3D sound localization so as to operate real-time on a low power embedded DSP This algorithm aims at providing a listener with the 3D sound effects through a headphone at low cost and low power consumption.

• The Journal of the Acoustical Society of Korea
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• v.28 no.4
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• pp.318-327
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• 2009

In this paper, we propose a 3-D geometric localization method for near-field broadband source in shallow water environments. According to the waveguide invariant theory, slope of the interference pattern which is seen in a sensor spectrogram directly proportional to a range of the source. The relative ratio of the range between source and sensors was estimated by matching of two interference patterns in spectrogram. Then this ratio is applied to the Apollonius's circle which shows the locus of a source whose range ratio from two sensors is constant. Two Apollonius's circles from three sensors make the intersection point that means the horizontal range and the azimuth angle of the source. And this intersection point is constant with source depth. Therefore the source depth can be estimated using 3-D hyperboloid equation whose range difference from two sensors is constant. To evaluate a performance of the proposed localization algorithm, simulation is performed using acoustic propagation program and analysis of localization error is demonstrated. From simulation results, error estimate for range and depth is described within 50 m and 15 m respectively.

• Journal of the Korea Institute of Military Science and Technology
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• v.5 no.2
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• pp.169-184
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• 2002

In order to extract bearing information from the directional sensors of DIFAR(directional frequency analysis and recording) that is a kind of passive sonobuoy, the cardioid beamforming algorithm applicable to DIFAR system was studied in the frequency domain. the algorithm uses narrow-band signals propagated though the media from the acoustic sources such as ship machineries. The proposed algorithm is expected to give signal to noise ratio of 6dB when it uses the maximum response axis(MRA) among the Cardioid beams. The estimated bearings agree very well with those from GPS data. Assuming the bearings from GPS data to be real values, the estimation errors are analyzed statistically. The histogram of estimation errors in each frequency have Gaussian shape, the mean and standard deviation dropping in the ranges -1.1~$6.7^{\circ}$ and 13.3~$43.6^{\circ}$, respectively. Estimation errors are caused by SMR degradation due to propagation loss between the source and receiver, daily fluctuating geo-magnetic fields, and non-stationary background noises. If multiple DIFAR systems are employed, in addition to bearing, range information could be estimated and finally localization or tracking of a target is possible.