scholarly journals Spherical Reverse Beamforming for Sound Source Localization Based on the Inverse Method

Sensors ◽  
2019 ◽  
Vol 19 (11) ◽  
pp. 2618 ◽  
Author(s):  
Chao Sun ◽  
Yuechan Liu
Keyword(s):  
Source Localization ◽  
Sound Source ◽  
Background Noise ◽  
Inverse Method ◽  
Optimization Problem ◽  
Sound Field ◽  
Sound Source Localization ◽  
Sound Sources ◽  
Sound Field Reconstruction ◽  
Array Aperture

A spherical array is not limited to providing an acoustic map in all directions by the azimuth of the array. In this paper, spherical reverse beamforming for sound source localization based on spherical harmonic beamforming and the principle of sound field reconstruction is proposed in order to output a sharper scanning beam. It is assumed that there is an imaginary sound source at each scan point, and the acoustic map of a spherical array to the actual sound source is regarded as the combination of all of the imaginary sound sources. Sound source localization can be realized by calculating the contribution of each imaginary sound source to the sound field. Also in this work, the non-convex constrained optimization problem is established using p-norm. Combined with the norm method, the sparse solution of the imaginary sources is obtained through iterative weighted techniques, and the resolution of sound source localization is improved significantly. The performance of this method is investigated in comparison to conventional spherical beamforming. The numerical results show that the proposed method can achieve higher resolution for the localization of sound sources without being limited by the frequency and array aperture, and has a stronger ability to suppress fluctuations in background noise.

scholarly journals Interior Sound Field Subjective Evaluation Based on the 3D Distribution of Sound Quality Objective Parameters and Sound Source Localization

Materials ◽  
2021 ◽  
Vol 14 (2) ◽  
pp. 429
Author(s):  
Jiangming Jin ◽  
Hao Cheng ◽  
Tianwei Xie ◽  
Huancai Lu
Keyword(s):  
Source Localization ◽  
Sound Source ◽  
Sound Pressure ◽  
Low Frequency ◽  
Sound Field ◽  
Sound Quality ◽  
Sound Source Localization ◽  
Frequency Noise ◽  
Sound Sources ◽  
Quality Objective

Controlling low frequency noise in an interior sound field is always a challenge in engineering, because it is hard to accurately localize the sound source. Spherical acoustic holography can reconstruct the 3D distributions of acoustic quantities in the interior sound field, and identify low-frequency sound sources, but the ultimate goal of controlling the interior noise is to improve the sound quality in the interior sound field. It is essential to know the contributions of sound sources to the sound quality objective parameters. This paper presents the mapping methodology from sound pressure to sound quality objective parameters, where sound quality objective parameters are calculated from sound pressure at each specific point. The 3D distributions of the loudness and sharpness are obtained by calculating each point in the entire interior sound field. The reconstruction errors of those quantities varying with reconstruction distance, sound frequency, and intersection angle are analyzed in numerical simulation for one- and two-monopole source sound fields. Verification experiments have been conducted in an anechoic chamber. Simulation and experimental results demonstrate that the sound source localization results based on 3D distributions of sound quality objective parameters are different from those based on sound pressure.

scholarly journals Towards Robust Multiple Blind Source Localization Using Source Separation and Beamforming

Sensors ◽  
2021 ◽  
Vol 21 (2) ◽  
pp. 532
Author(s):  
Henglin Pu ◽  
Chao Cai ◽  
Menglan Hu ◽  
Tianping Deng ◽  
Rong Zheng ◽  
...  
Keyword(s):  
Source Localization ◽  
Sound Source ◽  
Indoor Localization ◽  
Weighting Function ◽  
Signal To Noise Ratio ◽  
Source Separation ◽  
Sound Source Localization ◽  
Angle Of Arrival ◽  
Sound Sources ◽  
Localization Algorithms

Multiple blind sound source localization is the key technology for a myriad of applications such as robotic navigation and indoor localization. However, existing solutions can only locate a few sound sources simultaneously due to the limitation imposed by the number of microphones in an array. To this end, this paper proposes a novel multiple blind sound source localization algorithms using Source seParation and BeamForming (SPBF). Our algorithm overcomes the limitations of existing solutions and can locate more blind sources than the number of microphones in an array. Specifically, we propose a novel microphone layout, enabling salient multiple source separation while still preserving their arrival time information. After then, we perform source localization via beamforming using each demixed source. Such a design allows minimizing mutual interference from different sound sources, thereby enabling finer AoA estimation. To further enhance localization performance, we design a new spectral weighting function that can enhance the signal-to-noise-ratio, allowing a relatively narrow beam and thus finer angle of arrival estimation. Simulation experiments under typical indoor situations demonstrate a maximum of only 4∘ even under up to 14 sources.

Denoising Weighting Beamforming Method Applied to Sound Source Localization With Airflow Using Microphone Array

2018 ◽  
Vol 140 (6) ◽  
Author(s):  
Zhongming Xu ◽  
Kai Tian ◽  
Yansong He ◽  
Zhifei Zhang ◽  
Shu Li
Keyword(s):  
Source Localization ◽  
Sound Source ◽  
Background Noise ◽  
Sound Propagation ◽  
Phase Mismatch ◽  
Sound Source Localization ◽  
Time Averaging ◽  
Iterative Regularization ◽  
Spectral Matrix ◽  
Incoherent Noise

Conventional frequency domain beamforming (FDBF) relies on the measured cross-spectral matrix (CSM). However, in wind tunnel tests, the CSM diagonal is contaminated by the interference of incoherent noise after long-time averaging which leads the source map to poor resolution. Diagonal removal (DR) can suppress the noise in beamforming results via the deletion of CSM diagonal, but this method leads to the underestimation of source levels and some negative powers in source maps. Some advanced methods, such as background subtraction, make use of background noise reference to counteract the effects of contamination; however, the results usually become unreliable, because the background noise is difficult to keep constant in different measurements. Diagonal denoising (DD) beamforming is a recent approach to suppress the contamination effects, but it attenuates the noise suppression performance. To overcome the limitations of the above methods, a new method called denoising weighting beamforming (DWB) is proposed in this study on the basis of CSM DD and an iterative regularization method is applied to solve the acoustical inverse problem. Besides, in order to correct the phase mismatch caused by the influence of flow on sound propagation, the shear flow correction is added before using DWB. Experiments on sound source reconstruction are conducted in the environment with the flow. Acoustics data obtained via this method show the successful removal of incoherent noise and the corrected phase mismatch. Furthermore, the sound source localization results are promising and the proposed method is simple to implement.

Phase calibration method for microphone array based on multiple sound sources

2021 ◽  
Vol 263 (6) ◽  
pp. 659-669
Author(s):  
Bo Jiang ◽  
XiaoQin Liu ◽  
Xing Wu
Keyword(s):  
Nonlinear Equation ◽  
Source Localization ◽  
Sound Source ◽  
Microphone Array ◽  
Phase Error ◽  
Phase Mismatch ◽  
Single Frequency ◽  
Sound Source Localization ◽  
Sound Sources ◽  
Error Calibration

In the microphone array, the phase error of each microphone causes a deviation in sound source localization. At present, there is a lack of effective methods for phase error calibration of the entire microphone array. In order to solve this problem, a phase mismatch calculation method based on multiple sound sources is proposed. This method requires collecting data from multiple sound sources in turn, and constructing a nonlinear equation setthrough the signal delay and the geometric relationship between the microphones and the sound source positions. The phase mismatch of each microphone can be solved from the nonlinear equation set. Taking the single frequency signal as an example, the feasibility of the method is verified by experiments in a semi-anechoic chamber. The phase mismatches are compared with the calibration results of exchanging microphone. The difference of the phase error values measured by the two methods is small. The experiment also shows that the accuracy of sound source localization by beamforming is improved. The method is efficient for phase error calibration of arrays with a large number of microphones.

Spatial Hearing and Functional Auditory Skills in Children With Unilateral Hearing Loss

2021 ◽  
pp. 1-18
Author(s):  
Nicole E. Corbin ◽  
Emily Buss ◽  
Lori J. Leibold
Keyword(s):  
Hearing Loss ◽  
Speech Recognition ◽  
Source Localization ◽  
Sound Source ◽  
Sound Field ◽  
Spatial Hearing ◽  
Parent Report ◽  
Sound Source Localization ◽  
Unilateral Hearing Loss ◽  
Auditory Performance

Purpose The purpose of this study was to characterize spatial hearing abilities of children with longstanding unilateral hearing loss (UHL). UHL was expected to negatively impact children's sound source localization and masked speech recognition, particularly when the target and masker were separated in space. Spatial release from masking (SRM) in the presence of a two-talker speech masker was expected to predict functional auditory performance as assessed by parent report. Method Participants were 5- to 14-year-olds with sensorineural or mixed UHL, age-matched children with normal hearing (NH), and adults with NH. Sound source localization was assessed on the horizontal plane (−90° to 90°), with noise that was either all-pass, low-pass, high-pass, or an unpredictable mixture. Speech recognition thresholds were measured in the sound field for sentences presented in two-talker speech or speech-shaped noise. Target speech was always presented from 0°; the masker was either colocated with the target or spatially separated at ±90°. Parents of children with UHL rated their children's functional auditory performance in everyday environments via questionnaire. Results Sound source localization was poorer for children with UHL than those with NH. Children with UHL also derived less SRM than those with NH, with increased masking for some conditions. Effects of UHL were larger in the two-talker than the noise masker, and SRM in two-talker speech increased with age for both groups of children. Children with UHL whose parents reported greater functional difficulties achieved less SRM when either masker was on the side of the better-hearing ear. Conclusions Children with UHL are clearly at a disadvantage compared with children with NH for both sound source localization and masked speech recognition with spatial separation. Parents' report of their children's real-world communication abilities suggests that spatial hearing plays an important role in outcomes for children with UHL.

Influence of Different Impulse Response Measurement Signals on MUSIC-Based Sound Source Localization

2017 ◽  
Vol 29 (1) ◽  
pp. 72-82 ◽  
Author(s):  
Takuya Suzuki ◽  
◽  
Hiroaki Otsuka ◽  
Wataru Akahori ◽  
Yoshiaki Bando ◽  
...  
Keyword(s):  
Impulse Response ◽  
Source Localization ◽  
Sound Source ◽  
Transfer Functions ◽  
Signal To Noise Ratio ◽  
Microphone Array ◽  
Sound Source Localization ◽  
Sound Sources ◽  
Response Measurement ◽  
Significant Difference

[abstFig src='/00290001/07.jpg' width='300' text='Six impulse response measurement signals' ] Two major functions, sound source localization and sound source separation, provided by robot audition open source software HARK exploit the acoustic transfer functions of a microphone array to improve the performance. The acoustic transfer functions are calculated from the measured acoustic impulse response. In the measurement, special signals such as Time Stretched Pulse (TSP) are used to improve the signal-to-noise ratio of the measurement signals. Recent studies have identified the importance of selecting a measurement signal according to the applications. In this paper, we investigate how six measurement signals – up-TSP, down-TSP, M-Series, Log-SS, NW-SS, and MN-SS – influence the performance of the MUSIC-based sound source localization provided by HARK. Experiments with simulated sounds, up to three simultaneous sound sources, demonstrate no significant difference among the six measurement signals in the MUSIC-based sound source localization.

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