scholarly journals Acoustic source localization using the open spherical microphone array in the low-frequency range

2019 ◽  
Vol 283 ◽  
pp. 04001
Author(s):  
Boquan Yang ◽  
Shengguo Shi ◽  
Desen Yang
Keyword(s):  
Source Localization ◽  
Generalized Inverse ◽  
Microphone Array ◽  
Low Frequency ◽  
Poor Performance ◽  
Microphone Arrays ◽  
Three Dimension ◽  
Frequency Range ◽  
Array Aperture ◽  
Regularization Matrix

Recently, spherical microphone arrays (SMA) have become increasingly significant for source localization and identification in three dimension due to its spherical symmetry. However, conventional Spherical Harmonic Beamforming (SHB) based on SMA has limitations, such as poor resolution and high side-lobe levels in image maps. To overcome these limitations, this paper employs the iterative generalized inverse beamforming algorithm with a virtual extrapolated open spherical microphone array. The sidelobes can be suppressed and the main-lobe can be narrowed by introducing the two iteration processes into the generalized inverse beamforming (GIB) algorithm. The instability caused by uncertainties in actual measurements, such as measurement noise and configuration problems in the process of GIB, can be minimized by iteratively redefining the form of regularization matrix and the corresponding GIB localization results. In addition, the poor performance of microphone arrays in the low-frequency range due to the array aperture can be improved by using a virtual extrapolated open spherical array (EA), which has a larger array aperture. The virtual array is obtained by a kind of data preprocessing method through the regularization matrix algorithm. Both results from simulations and experiments show the feasibility and accuracy of the method.

Assessment of MUSIC-Based Noise-Robust Sound Source Localization with Active Frequency Range Filtering

2018 ◽  
Vol 30 (3) ◽  
pp. 426-435 ◽  
Author(s):  
Kotaro Hoshiba ◽  
Kazuhiro Nakadai ◽  
Makoto Kumon ◽  
Hiroshi G. Okuno ◽  
◽  
...  
Keyword(s):  
Real Time ◽  
Source Localization ◽  
Sound Source ◽  
Microphone Array ◽  
Sound Source Localization ◽  
Noise Robustness ◽  
Frequency Range ◽  
Real Time Processing ◽  
Time Processing ◽  
Localization Method

We have studied sound source localization, using a microphone array embedded on a UAV (unmanned aerial vehicle), for the purpose of detecting for people to rescue from disaster-stricken areas or other dangerous situations, and we have proposed sound source localization methods for use in outdoor environments. In these methods, noise robustness and real-time processing have a trade-off relationship, which is a problem to be solved for the practical application of the methods. Sound source localization in a disaster area requires both noise robustness and real-time processing. For this we propose a sound source localization method using an active frequency range filter based on the MUSIC (MUltiple Signal Classification) method. Our proposed method can successively create and apply a frequency range filter by simply using the four arithmetic operations, so it can ensure both noise robustness and real-time processing. As numerical simulations carried out to compare the successful localization rate and the processing delay with conventional methods have affirmed the usefulness of the proposed method, we have successfully produced a sound source localization method that has both noise robustness and real-time processing.

Functional generalized inverse beamforming with regularization matrix applied to sound source localization

2016 ◽  
Vol 23 (18) ◽  
pp. 2977-2988 ◽  
Author(s):  
Shu Li ◽  
Zhongming Xu ◽  
Zhifei Zhang ◽  
Yansong He ◽  
Jin Mao
Keyword(s):  
Source Localization ◽  
Dynamic Range ◽  
Generalized Inverse ◽  
Free Field ◽  
Computation Time ◽  
Side Lobe ◽  
Sound Sources ◽  
Low Frequencies ◽  
Common Application ◽  
Regularization Matrix

Microphone arrays have become a popular technique to identify sound sources. They can be utilized to localize the sources for various applications. The most common application is the conventional beamforming that provides the source maps with strong side lobes and poor spatial resolution at low frequencies. To overcome these problems, the focus is set on deconvolution and generalized inverse techniques such as a deconvolution approach for the mapping of acoustic sources (DAMAS) and generalized inverse beamforming (GIB). Although the source maps are clearly improved, these methods have the shortcomings of expensive computing and limited dynamic range. In this paper, we propose a source localization method called functional generalized inverse beamforming with regularization matrix (FGIBR) based on an inverse problem. Compared with GIB, the accuracy of FGIBR could be improved by introducing a new beamforming regularization matrix and a scaling parameter c0. Also the dynamic range of the source maps can be increased by applying FGIBR with an exponent parameter called order v. Several simulated examples are given to illustrate that the side lobes are suppressed and the main lobe becomes much narrow; moreover, if order v is increased, the beamforming side lobes can be sharply reduced and the actual position of the noise source can be precisely located. Then FGIBR is implemented to deal with experimental data in the free field. In the case of the experiment, the source is correctly located. The proposed FGIBR demonstrates a good performance in terms of resolution and side lobe rejection compared with other beamforming methods. Furthermore, the computation time is shown to be low if the iteration and order are reasonable.

scholarly journals Design Considerations When Accelerating an FPGA-Based Digital Microphone Array for Sound-Source Localization

2017 ◽  
Vol 2017 ◽  
pp. 1-20 ◽  
Author(s):  
Bruno da Silva ◽  
An Braeken ◽  
Kris Steenhaut ◽  
Abdellah Touhafi
Keyword(s):  
Real Time ◽  
Source Localization ◽  
Sound Source ◽  
Angular Resolution ◽  
Microphone Array ◽  
Sensor Arrays ◽  
Microphone Arrays ◽  
Sound Source Localization ◽  
Design Considerations ◽  
Acceleration Techniques

The use of microphone arrays for sound-source localization is a well-researched topic. The response of such sensor arrays is dependent on the quantity of microphones operating on the array. A higher number of microphones, however, increase the computational demand, making real-time response challenging. In this paper, we present a Filter-and-Sum based architecture and several acceleration techniques to provide accurate sound-source localization in real-time. Experiments demonstrate how an accurate sound-source localization is obtained in a couple of milliseconds, independently of the number of microphones. Finally, we also propose different strategies to further accelerate the sound-source localization while offering increased angular resolution.

Functional Generalized Inverse Beamforming Based on the Double-Layer Microphone Array Applied to Separate the Sound Sources

2016 ◽  
Vol 138 (2) ◽  
Author(s):  
Shu Li ◽  
Zhongming Xu ◽  
Yansong He ◽  
Zhifei Zhang ◽  
Shaoyu Song
Keyword(s):  
Double Layer ◽  
Dynamic Range ◽  
Generalized Inverse ◽  
Microphone Array ◽  
A Priori ◽  
Sound Sources ◽  
Popular Method ◽  
Array Measurements ◽  
Two Sides ◽  
Regularization Matrix

Beamforming based on microphone array measurements is a popular method for identifying sound sources. However, beamforming has many limitations that limit their precision. These limitations are addressed in research. To separate the contributions which come from two sides of the microphone array more accurately, an innovative beamforming method based on a double-layer microphone array, called functional generalized inverse beamforming (FGIB), is proposed to improve beamforming performance. This method, which involves the use of a priori beamforming regularization matrix and a matrix function to redefine the inverse problem, is combined with the advantages of both generalized inverse beamforming (GIB) and functional beamforming. Compared with GIB, with reduced iterations, the computational efficiency of FGIB is greatly improved. The dynamic range of the proposed method can be modestly improved as order v increases. Furthermore, the sidelobes gradually disappear and the mainlobes become narrower. Both simulations and experiments have shown that the sources are correctly located and separated. The proposed FGIB demonstrates the good performance when compared to other beamforming methods in terms of resolution and sidelobes level.

scholarly journals Microphone Arrays as a Leakage Detection Tool in Industrial Compressed Air Systems

2012 ◽  
Vol 2012 ◽  
pp. 1-10 ◽  
Author(s):  
Petr Eret ◽  
Craig Meskell
Keyword(s):  
Best Practice ◽  
Microphone Array ◽  
Leak Detection ◽  
Microphone Arrays ◽  
Compressed Air ◽  
Handheld Devices ◽  
Ultrasonic Frequency ◽  
Noisy Environment ◽  
Frequency Range ◽  
Industrial Manufacturing

Compressed air energy is expensive, but common in industrial manufacturing plant. However, a significant part of the generated compressed air energy is lost due to leakage. Best practice requires ongoing leak detection and repair. Leak detection in the ultrasonic frequency range using handheld devices is possible only over short distances as associated high-frequency sound is rapidly attenuated by atmospheric absorption. Pressurized air escaping to ambience also generates frequencies below 20 kHz. In this paper beamforming—a well known method for generating noise maps—is tested as a tool for localization of compressed air leaks at larger distances in the audible frequency range. Advanced beamforming methods in both time domain (broadband) and frequency domain (narrowband) have been implemented in a variety of situations on a laboratory experimental rig with several open blows representing leakage in a noisy environment similar to a factory setting. Based on the results achieved it is concluded that the microphone array approach has the potential to be a robust leak identification tool. The experience gained here can also provide useful guidance to the practitioner.

scholarly journals DATASET FOR EVALUATION OF THE PERFORMANCE OF THE METHODS OF SOUND SOURCE LOCALIZATION ALGORITHMS USING TETRAHEDRAL MICROPHONE ARRAYS

2020 ◽  
Vol 12 (0) ◽  
pp. 1-8
Author(s):  
Saulius Sakavičius
Keyword(s):  
Source Localization ◽  
Sound Source ◽  
Microphone Array ◽  
Direction Of Arrival ◽  
Microphone Arrays ◽  
Sound Source Localization ◽  
Direction Of Arrival Estimation ◽  
Sound Sources ◽  
Localization Algorithms ◽  
Geometrical Information

For the development and evaluation of a sound source localization and separation methods, a concise audio dataset with complete geometrical information about the room, the positions of the sound sources, and the array of microphones is needed. Computer simulation of such audio and geometrical data often relies on simplifications and are sufficiently accurate only for a specific set of conditions. It is generally desired to evaluate algorithms on real-world data. For a three-dimensional sound source localization or direction of arrival estimation, a non-coplanar microphone array is needed.Simplest and most general type of non-coplanar array is a tetrahedral array. There is a lack of openly accessible realworld audio datasets obtained using such arrays. We present an audio dataset for the evaluation of sound source localization algorithms, which involve tetrahedral microphone arrays. The dataset is complete with the geometrical information of the room, the positions of the sound sources and the microphone array. Array audio data was captured for two tetrahedral microphone arrays with different distances between microphones and one or two active sound sources. The dataset is suitable for speech recognition and direction-of-arrival estimation, as the signals used for sound sources were speech signals.

Evaluation of Special Hearing Aids for Deaf Children

1971 ◽  
Vol 36 (4) ◽  
pp. 527-537 ◽  
Author(s):  
Norman P. Erber
Keyword(s):  
Hearing Aids ◽  
High Frequency ◽  
Hearing Aid ◽  
Low Frequency ◽  
Acoustic Stimulation ◽  
Deaf Children ◽  
Frequency Range ◽  
Frequency Limit ◽  
Unpublished Studies ◽  
Published Research

Two types of special hearing aid have been developed recently to improve the reception of speech by profoundly deaf children. In a different way, each special system provides greater low-frequency acoustic stimulation to deaf ears than does a conventional hearing aid. One of the devices extends the low-frequency limit of amplification; the other shifts high-frequency energy to a lower frequency range. In general, previous evaluations of these special hearing aids have obtained inconsistent or inconclusive results. This paper reviews most of the published research on the use of special hearing aids by deaf children, summarizes several unpublished studies, and suggests a set of guidelines for future evaluations of special and conventional amplification systems.

Dynamic Damping and Stiffness Characteristics of the Rolling Tire

2001 ◽  
Vol 29 (4) ◽  
pp. 258-268 ◽  
Author(s):  
G. Jianmin ◽  
R. Gall ◽  
W. Zuomin
Keyword(s):  
Dynamic Behavior ◽  
Variable Parameter ◽  
Low Frequency ◽  
Vertical Load ◽  
Frequency Range ◽  
Inflation Pressure ◽  
Vehicle Simulation ◽  
Dynamic Damping ◽  
Speed Range ◽  
Stiffness Characteristics

Abstract A variable parameter model to study dynamic tire responses is presented. A modified device to measure terrain roughness is used to measure dynamic damping and stiffness characteristics of rolling tires. The device was used to examine the dynamic behavior of a tire in the speed range from 0 to 10 km/h. The inflation pressure during the tests was adjusted to 160, 240, and 320 kPa. The vertical load was 5.2 kN. The results indicate that the damping and stiffness decrease with velocity. Regression formulas for the non-linear experimental damping and stiffness are obtained. These results can be used as input parameters for vehicle simulation to evaluate the vehicle's driving and comfort performance in the medium-low frequency range (0–100 Hz). This way it can be important for tire design and the forecasting of the dynamic behavior of tires.

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