A Biologically Inspired Coupled Microphone Array for Sound Source Bearing Estimation

2017 ◽  
Vol 140 (1) ◽  
Author(s):  
Yaqiong Zhang ◽  
Ming Yang ◽  
Xinlei Zhu ◽  
Na Ta ◽  
Zhushi Rao
Keyword(s):  
Time Delay ◽  
Sound Source ◽  
Microphone Array ◽  
Time Delay Estimation ◽  
Estimation Accuracy ◽  
Coupling Mechanism ◽  
Mechanical Coupling ◽  
Bearing Estimation ◽  
Localization Ability ◽  
Output Filter

The Ormia ochracea, a species of parasitic fly, has a remarkable localization ability despite the tiny interaural distance compared with the incoming wavelength. The mechanical coupling between its ears enhances the differences of the two received signals, the main cues to locate the source. Inspired by the coupling mechanism, we present a miniature coupled two-microphone array for estimating sound source horizontal bearing. The coupled array consists of a standard two-microphone array and a two-input, two-output filter which implements the coupling. The relationship between filter parameters and time delay magnification is investigated to provide theoretical support for array design. With appropriate parameters, the time delay of received signals can be linearly magnified. Based on the linear magnification, we present a method for estimating source direction using the coupled array. The influence of time delay magnification on time delay estimation accuracy is explored through the general cross-correlation (GCC) method. Experiments are conducted to verify the coupled array and demonstrate its advantages on improving the resolution of estimation of time delay and accuracy of bearing estimation compared with the standard array with the same element spacing.

Research on Sound Source Localization System Based on Time Delay Estimation

2013 ◽  
Vol 397-400 ◽  
pp. 2209-2214
Author(s):  
Chuan Yi Zhang ◽  
Chang Wei Mi ◽  
Pei Yang Yao
Keyword(s):  
Time Delay ◽  
Source Localization ◽  
Sound Source ◽  
Cross Correlation ◽  
Microphone Array ◽  
Estimation Method ◽  
Correlation Method ◽  
Time Delay Estimation ◽  
Delay Estimation ◽  
Sound Source Localization

In the estimation of time delay, there always would not appear obvious peak with the basic cross-correlation (CC). In order to solve the problem of the basic cross-correlation method, this essay represents an improved time delay estimation method based on the generalized cross-correlation (GCC) and combines with the microphone array structure to achieve sound source localization. Finally, the simulation results show that this method could measure the sound source’s location accurately with noise and reverberation, and the distance positioning error is less than 10cm, the direction angle error is below 3°.

scholarly journals Azimuth Tracking of Underwater Moving Sound Source Based on Time Delay Estimation Using Hydrophone Array

2017 ◽  
Vol 170 ◽  
pp. 169-176 ◽  
Author(s):  
Dhany Arifianto ◽  
Wirawan ◽  
B.T. Atmaja ◽  
Tutug Dhanardhono ◽  
Saptian A. Rahman
Keyword(s):  
Time Delay ◽  
Sound Source ◽  
Time Delay Estimation ◽  
Delay Estimation ◽  
Moving Sound Source ◽  
Hydrophone Array

scholarly journals Sound Source Localization Fusion Algorithm and Performance Analysis of a Three-Plane Five-Element Microphone Array

2019 ◽  
Vol 9 (12) ◽  
pp. 2417
Author(s):  
Hongyan Xing ◽  
Xu Yang
Keyword(s):  
Time Delay ◽  
Source Localization ◽  
Sound Source ◽  
Microphone Array ◽  
Elevation Angle ◽  
Measurement Precision ◽  
Sound Source Localization ◽  
Fusion Algorithm ◽  
Single Plane ◽  
Horizontal Angle

To reduce the negative effect on sound source localization when the source is at an extreme angle and improve localization precision and stability, a theoretical model of a three-plane five-element microphone array is established, using time-delay values to judge the sound source’s quadrant position. Corresponding judgment criteria were proposed, solving the problem in which a single-plane array easily blurs the measured position. Based on sound source geometric localization, a formula for the sound source azimuth calculation of a single-plane five-element microphone array was derived. The sinusoids and cosines of two elevation angles based on two single-plane arrays were introduced into the sound source spherical coordinates as composite weighted coefficients, and a sound source localization fusion algorithm based on a three-plane five-element microphone array was proposed. The relationship between the time-delay estimation error, elevation angle, horizontal angle, and microphone array localization performance was discussed, and the precision and stability of ranging and direction finding were analyzed. The results show that the measurement precision of the distance from the sound source to the array center and the horizontal angle are improved one to threefold, and the measurement precision of the elevation angle is improved one to twofold. Although there is a small error, the overall performance of the sound source localization is stable, reflecting the advantages of the fusion algorithm.

Realization of Algorithm for Wideband Sound Source Localization in Video Monitoring System

2013 ◽  
Vol 416-417 ◽  
pp. 1086-1091
Author(s):  
Lei Li ◽  
Yong Gang Su ◽  
Shen Tian ◽  
Yong Li ◽  
Zhi Tong Li
Keyword(s):  
Embedded System ◽  
Sound Source ◽  
Social Research ◽  
Microphone Array ◽  
Blind Spot ◽  
Video Tracking ◽  
Time Delay Estimation ◽  
Video Monitoring ◽  
Localization Algorithm ◽  
Security Monitoring

Video security monitoring has become the focus of social research and development; however, since the camera cannot automatically rotate, there is a blind spot in traditional security monitoring. Considering the abnormal often happens accompanied by corresponding sounds (e.g., where there is an explosion , there will be the sound of explosions), therefore, for compensating the blind spot , the auditory function can be added to the camera to track the direction of sound source automatically which requires the two-dimensional (2-D) localization of sound source to complete , at the mean time , the localization algorithm should be capable of tracking all of the source signals ,as well as be real-time to make the video tracking to be achieved by turning the camera toward sound source timely. This paper realizes the localization of wideband speech signal in video monitoring by using modern signal processing method, linear microphone array, positioning thought based on time delay estimation, frequency domain transform, and spectrum-search method based on energy value. Both the early simulation and late DSP-based embedded system platform have verified the feasibility of the method.

Performance of time-delay estimators

Geophysics ◽  
2005 ◽  
Vol 70 (4) ◽  
pp. V109-V120 ◽  
Author(s):  
Claudio Bagaini
Keyword(s):  
Time Delay ◽  
Time Delays ◽  
Signal To Noise Ratio ◽  
Linear Equations ◽  
Reference Signal ◽  
Synthetic Data ◽  
Original Data ◽  
Small Time ◽  
Time Delay Estimation ◽  
Estimation Accuracy

I analyze the problem of estimating differences in the arrival times of a seismic wavefront recorded by an array of sensors. The two-sensor problem is tackled first, showing that even an approximate knowledge of the wavelet, such as its power spectrum, can substantially increase the accuracy of the time-delay estimate and reduce the signal-to-noise ratio (S/N) threshold for reliable time-delay estimation. The use of the complex trace, although beneficial for time-delay estimates in the presence of frequency-independent phase shifts, reduces the estimation accuracy in poor S/N conditions. I compare the performance of five time-delay estimators for arrays of sensors. Four of five estimators are based on crosscorrelation with a reference signal derived according to one of the following criteria: one trace in the array randomly selected, the stack of all array traces, the stack of all array traces iteratively updated, and (possible only for synthetic data) the noise-free wavelet. Another method, which is referred to as integration of differential delays, is based on the solution of an overdetermined system of linear equations built using the time delays between each pair of sensors. In all the situations considered, the performance of crosscorrelation with a trace of the array randomly selected is significantly worse than the other methods. Integration of differential delays proved to be the best-performing method for a large range of S/N conditions, particularly in the presence of large fluctuations in time delays and large bandwidth. However, for small time delays with respect to the wavelet duration, or if a priori knowledge of the moveout can be used to detrend the original data, crosscorrelation with a stacked trace performs similarly to integration of differential delays.

scholarly journals Envelope and Wavelet Transform for Sound Localisation at Low Sampling Rates in Wireless Sensor Networks

2012 ◽  
Vol 2012 ◽  
pp. 1-9 ◽  
Author(s):  
O. M. Bouzid ◽  
G. Y. Tian ◽  
J. Neasham ◽  
B. Sharif
Keyword(s):  
Wavelet Transform ◽  
Time Delay ◽  
Sampling Rate ◽  
Time Delay Estimation ◽  
Wireless Sensor ◽  
Estimation Accuracy ◽  
Memory Space ◽  
Time Frequency ◽  
Sound Localisation ◽  
Huge Data

High sampling frequencies in acoustic wireless sensor network (AWSN) are required to achieve precise sound localisation. But they are also mean analysis time and memory intensive (i.e., huge data to be processed and more memory space to be occupied which form a burden on the nodes limited resources). Decreasing sampling rates below Nyquist criterion in acoustic source localisation (ASL) applications requires development of the existing time delay estimation techniques in order to overcome the challenge of low time resolution. This work proposes using envelope and wavelet transform to enhance the resolution of the received signals through the combination of different time-frequency contents. Enhanced signals are processed using cross-correlation in conjunction with a parabolic fit interpolation to calculate the time delay accurately. Experimental results show that using this technique, estimation accuracy was improved by almost a factor of 5 in the case of using 4.8 kHz sampling rate. Such a conclusion is useful for developing precise ASL without the need of any excessive sensor resources, particularly for structural health monitoring applications.

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