Rotating sound sources and listeners: Sound source localization is a multisensory/cognitive process

2015 ◽  
Vol 137 (4) ◽  
pp. 2200-2201 ◽  
Author(s):  
William Yost ◽  
Xuan Zhong ◽  
Anbar Najam
Keyword(s):  
Source Localization ◽  
Sound Source ◽  
Cognitive Process ◽  
Sound Source Localization ◽  
Sound Sources

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.

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.

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.

scholarly journals Compressing Sensing Based Source Localization for Controlled Acoustic Signals Using Distributed Microphone Arrays

2017 ◽  
Vol 2017 ◽  
pp. 1-11
Author(s):  
Wei Ke ◽  
Xiunan Zhang ◽  
Yanan Yuan ◽  
Jianhua Shao
Keyword(s):  
Source Localization ◽  
Sound Source ◽  
Reconstruction Algorithm ◽  
Acoustic Signals ◽  
Substantial Improvement ◽  
Microphone Arrays ◽  
Sound Source Localization ◽  
Sound Sources ◽  
Reconstruction Performance ◽  
Long Time

In order to enhance the accuracy of sound source localization in noisy and reverberant environments, this paper proposes an adaptive sound source localization method based on distributed microphone arrays. Since sound sources lie at a few points in the discrete spatial domain, our method can exploit this inherent sparsity to convert the localization problem into a sparse recovery problem based on the compressive sensing (CS) theory. In this method, a two-step discrete cosine transform- (DCT-) based feature extraction approach is utilized to cover both short-time and long-time properties of acoustic signals and reduce the dimensions of the sparse model. In addition, an online dictionary learning (DL) method is used to adjust the dictionary for matching the changes of audio signals, and then the sparse solution could better represent location estimations. Moreover, we propose an improved block-sparse reconstruction algorithm using approximate l0 norm minimization to enhance reconstruction performance for sparse signals in low signal-noise ratio (SNR) conditions. The effectiveness of the proposed scheme is demonstrated by simulation results and experimental results where substantial improvement for localization performance can be obtained in the noisy and reverberant conditions.

scholarly journals DESIGNING PLANAR MICROPHONE ARRAY FOR SOUND SOURCE LOCALIZATION

Akustika ◽  
2019 ◽  
Vol 32 ◽  
pp. 123-129 ◽  
Author(s):  
Victor Ershov ◽  
Vadim Palchikovskiy
Keyword(s):  
Inclination Angle ◽  
Source Localization ◽  
Sound Source ◽  
Dynamic Range ◽  
Microphone Array ◽  
Sound Source Localization ◽  
Maximum Diameter ◽  
Design Parameters ◽  
Sound Sources ◽  
The Individual

Mathematical background for designing planar microphone array for localization of sound sources are described shortly. The designing is based on optimization of objective function, which is maximum dynamic range of sound source localization. The design parameters are radial coordinates (distance along the beam from the center of the array) and angle coordinates (beam inclination) of the microphones. It is considered the arrays with the same radial coordinates of the microphones for each beam and the independent radial coordinates of each microphone, as well as the same inclination angle for all beams and the individual inclination angle of each beam. As constraints, it is used the minimum allowable distance between two adjacent microphones, and minimum and maximum diameter of the working area of the array. The solution of the optimization problem is performed by the Minimax method. An estimation of the resolution quality of designed arrays was carried out based on localization of three monopole sources. The array of 3 m in diameter without inclination of the beams and with different radial coordinates of the microphones on each beam was found to be the most efficient configuration among the considered ones.

Efficient Energy-based Orthogonal Matching Pursuit Algorithm for Multiple Sound Source Localization with Unknown Source Count

2021 ◽  
Author(s):  
Rongjiang Tang ◽  
Yingxiang zuo ◽  
Weiya Liu ◽  
Liguo Tang ◽  
Weiguang Zheng ◽  
...  
Keyword(s):  
Compressed Sensing ◽  
Source Localization ◽  
Sound Source ◽  
Matching Pursuit ◽  
Orthogonal Matching Pursuit ◽  
Sound Source Localization ◽  
Signal Frequency ◽  
Localization Algorithm ◽  
Sound Sources ◽  
Signal Energy

Abstract In this paper, we propose a compressed sensing (CS) sound source localization algorithm based on signal energy to solve the problem of stopping iteration condition of orthogonal matching pursuit reconstruction algorithm in compressed sensing. The orthogonal matching tracking algorithm needs to stop iteration according to the number of sound sources or the change of residual. Generally, the number of sound sources cannot be known in advance, and the residual often leads to unnecessary calculation. Because the sound source is sparsely distributed in space, and its energy is concentrated and higher than that of the environmental noise, the comparison of the signal energy at different positions in each iteration reconstruction signal is used to determine whether the new sound source is added in this iteration. At the same time, the block sparsity is introduced by using multiple frequency points to avoid the problem of different iteration times of different frequency points in the same frame caused by the uneven energy distribution in the signal frequency domain. Simulation and experimental results show that the proposed algorithm retains the advantages of the orthogonal matching tracking sound source localization algorithm, and can complete the iteration well. Under the premise of not knowing the number of sound sources, the maximum error between the number of iterations and the set number of sound sources is 0.31.

scholarly journals Position-dependent hearing in three species of bushcrickets (Tettigoniidae, Orthoptera)

2015 ◽  
Vol 2 (6) ◽  
pp. 140473 ◽  
Author(s):  
Reinhard Lakes-Harlan ◽  
Jan Scherberich
Keyword(s):  
Source Localization ◽  
Sound Source ◽  
Transfer Functions ◽  
Frequency Tuning ◽  
Three Dimensional ◽  
Sound Source Localization ◽  
Sound Sources ◽  
Ear Morphology ◽  
Hearing Thresholds ◽  
Species Specific

A primary task of auditory systems is the localization of sound sources in space. Sound source localization in azimuth is usually based on temporal or intensity differences of sounds between the bilaterally arranged ears. In mammals, localization in elevation is possible by transfer functions at the ear, especially the pinnae. Although insects are able to locate sound sources, little attention is given to the mechanisms of acoustic orientation to elevated positions. Here we comparatively analyse the peripheral hearing thresholds of three species of bushcrickets in respect to sound source positions in space. The hearing thresholds across frequencies depend on the location of a sound source in the three-dimensional hearing space in front of the animal. Thresholds differ for different azimuthal positions and for different positions in elevation. This position-dependent frequency tuning is species specific. Largest differences in thresholds between positions are found in Ancylecha fenestrata . Correspondingly, A. fenestrata has a rather complex ear morphology including cuticular folds covering the anterior tympanal membrane. The position-dependent tuning might contribute to sound source localization in the habitats. Acoustic orientation might be a selective factor for the evolution of morphological structures at the bushcricket ear and, speculatively, even for frequency fractioning in the ear.

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