Localisation of Sound Sources on Aircraft in Flight

2012 ◽  
Author(s):  
Henri A. Siller
Keyword(s):  
Dynamic Range ◽  
Microphone Array ◽  
Acoustic Power ◽  
Microphone Arrays ◽  
Sound Sources ◽  
Noise Abatement ◽  
Source Regions ◽  
The Time Domain ◽  
Imaging Properties ◽  
Source Signals

This paper presents beamforming techniques for source localization on aicraft in flight with a focus on the development at DLR in Germany. Fly-over tests with phased arrays are the only way to localize and analyze the different aerodynamic and engine sources of aircraft in flight. Many of these sources cannot be simulated numerically or in wind-tunnel tests because they they are either unknown or they cannot be resolved properly in model scale. The localization of sound sources on aircraft in flight is performed using large microphone arrays. For the data analysis, the source signals at emission time are reconstructed from the Doppler-shifted microphone data using the measured flight trajectory. Standard beamforming techniques in the frequency domain cannot be applied due transitory nature of the signals, so the data is usually analyzed using a classical beamforming algorithm in the time domain. The spatial resolution and the dynamic range of the source maps can be improved by calculating a deconvolution of the sound source maps with the point spread function of the microphone array. This compensates the imaging properties of the microphone array by eliminating side lobes and aliases. While classical beamfoming yields results that are more qualitative by nature, the deconvolution results can be used to integrate the acoustic power over the different source regions in order to obtain the powers of each source. ranking of the sources. These results can be used to rank the sources, for acoustic trouble shooting, and to assess the potential of noise abatement methods.

Layout Optimization of Cooperative Distributed Microphone Arrays Based on Estimation of Source Separation Performance

2017 ◽  
Vol 29 (1) ◽  
pp. 83-93
Author(s):  
Kouhei Sekiguchi ◽  
◽  
Yoshiaki Bando ◽  
Katsutoshi Itoyama ◽  
Kazuyoshi Yoshii
Keyword(s):  
Mobile Robots ◽  
Microphone Array ◽  
Source Separation ◽  
Microphone Arrays ◽  
Separation Performance ◽  
Sound Sources ◽  
Multiple Mobile Robots ◽  
Position Information ◽  
Reconfigurable Array ◽  
Source Signals

[abstFig src='/00290001/08.jpg' width='300' text='Optimizing robot positions for source separation' ] The active audition method presented here improves source separation performance by moving multiple mobile robots to optimal positions. One advantage of using multiple mobile robots that each has a microphone array is that each robot can work independently or as part of a big reconfigurable array. To determine optimal layout of the robots, we must be able to predict source separation performance from source position information because actual source signals are unknown and actual separation performance cannot be calculated. Our method thus simulates delay-and-sum beamforming from a possible layout to calculate gain theoretically, i.e., the expected ratio of a target sound source to other sound sources in the corresponding separated signal. Robots are moved into the layout with the highest average gain over target sources. Experimental results showed that our method improved the harmonic mean of signal-to-distortion ratios (SDRs) by 5.5 dB in simulation and by 3.5 dB in a real environment.

scholarly journals Bio-acoustic tracking and localization using heterogeneous, scalable microphone arrays

2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Erik Verreycken ◽  
Ralph Simon ◽  
Brandt Quirk-Royal ◽  
Walter Daems ◽  
Jesse Barber ◽  
...  
Keyword(s):  
Microphone Array ◽  
Microphone Arrays ◽  
Sound Sources ◽  
Acoustic Data ◽  
Array Architecture ◽  
Spatially Distributed ◽  
Animal Vocalizations ◽  
Pallid Bats ◽  
Tracking Device ◽  
And Behavior

AbstractMicrophone arrays are an essential tool in the field of bioacoustics as they provide a non-intrusive way to study animal vocalizations and monitor their movement and behavior. Microphone arrays can be used for passive localization and tracking of sound sources while analyzing beamforming or spatial filtering of the emitted sound. Studying free roaming animals usually requires setting up equipment over large areas and attaching a tracking device to the animal which may alter their behavior. However, monitoring vocalizing animals through arrays of microphones, spatially distributed over their habitat has the advantage that unrestricted/unmanipulated animals can be observed. Important insights have been achieved through the use of microphone arrays, such as the convergent acoustic field of view in echolocating bats or context-dependent functions of avian duets. Here we show the development and application of large flexible microphone arrays that can be used to localize and track any vocalizing animal and study their bio-acoustic behavior. In a first experiment with hunting pallid bats the acoustic data acquired from a dense array with 64 microphones revealed details of the bats’ echolocation beam in previously unseen resolution. We also demonstrate the flexibility of the proposed microphone array system in a second experiment, where we used a different array architecture allowing to simultaneously localize several species of vocalizing songbirds in a radius of 75 m. Our technology makes it possible to do longer measurement campaigns over larger areas studying changing habitats and providing new insights for habitat conservation. The flexible nature of the technology also makes it possible to create dense microphone arrays that can enhance our understanding in various fields of bioacoustics and can help to tackle the analytics of complex behaviors of vocalizing animals.

scholarly journals Capturing and Reproducing Spatial Audio Based on a Circular Microphone Array

2013 ◽  
Vol 2013 ◽  
pp. 1-16 ◽  
Author(s):  
Anastasios Alexandridis ◽  
Anthony Griffin ◽  
Athanasios Mouchtaris
Keyword(s):  
Side Information ◽  
Microphone Array ◽  
Audio Signal ◽  
Time Frame ◽  
Spatial Audio ◽  
Sound Sources ◽  
Acoustic Environment ◽  
Listening Tests ◽  
Source Signals ◽  
Array Recordings

This paper proposes a real-time method for capturing and reproducing spatial audio based on a circular microphone array. Following a different approach than other recently proposed array-based methods for spatial audio, the proposed method estimates the directions of arrival of the active sound sources on a per time-frame basis and performs source separation with a fixed superdirective beamformer, which results in more accurate modelling and reproduction of the recorded acoustic environment. The separated source signals are downmixed into one monophonic audio signal, which, along with side information, is transmitted to the reproduction side. Reproduction is possible using either headphones or an arbitrary loudspeaker configuration. The method is compared with other recently proposed array-based spatial audio methods through a series of listening tests for both simulated and real microphone array recordings. Reproduction using both loudspeakers and headphones is considered in the listening tests. As the results indicate, the proposed method achieves excellent spatialization and sound quality.

scholarly journals Time Domain Spherical Harmonic Processing with Open Spherical Microphones Recording

2021 ◽  
Vol 11 (3) ◽  
pp. 1074
Author(s):  
Huiyuan Sun ◽  
Thushara D. Abhayapala ◽  
Prasanga N. Samarasinghe
Keyword(s):  
Time Domain ◽  
Spherical Harmonic ◽  
Microphone Array ◽  
Sound Field ◽  
Microphone Arrays ◽  
Spatial Audio ◽  
Harmonic Decomposition ◽  
Spherical Harmonic Decomposition ◽  
The Time Domain ◽  
Spherical Microphone Arrays

Spherical harmonic analysis has been a widely used approach for spatial audio processing in recent years. Among all applications that benefit from spatial processing, spatial Active Noise Control (ANC) remains unique with its requirement for open spherical microphone arrays to record the residual sound field throughout the continuous region. Ideally, a low delay spherical harmonic recording algorithm for open spherical microphone arrays is desired for real-time spatial ANC systems. Currently, frequency domain algorithms for spherical harmonic decomposition of microphone array recordings are applied in a spatial ANC system. However, a Short Time Fourier Transform is required, which introduces undesirable system delay for ANC systems. In this paper, we develop a time domain spherical harmonic decomposition algorithm for the application of spatial audio recording mainly with benefit to ANC with an open spherical microphone array. Microphone signals are processed by a series of pre-designed finite impulse response (FIR) filters to obtain a set of time domain spherical harmonic coefficients. The time domain coefficients contain the continuous spatial information of the residual sound field. We corroborate the time domain algorithm with a numerical simulation of a fourth order system, and show the proposed method to have lower delay than existing approaches.

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.

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 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.

Separating Multiple Moving Sources by Microphone Array Signals for Wayside Acoustic Fault Diagnosis

2019 ◽  
Vol 141 (5) ◽  
Author(s):  
Wei Xiong ◽  
Qingbo He ◽  
Zhike Peng
Keyword(s):  
Fault Diagnosis ◽  
Time Domain ◽  
Feature Matching ◽  
Matching Pursuit ◽  
Microphone Array ◽  
Time Frequency ◽  
Moving Sources ◽  
The Time Domain ◽  
Envelope Spectrum ◽  
Threshold Setting

Wayside acoustic defective bearing detector (ADBD) system is a potential technique in ensuring the safety of traveling vehicles. However, Doppler distortion and multiple moving sources aliasing in the acquired acoustic signals decrease the accuracy of defective bearing fault diagnosis. Currently, the method of constructing time-frequency (TF) masks for source separation was limited by an empirical threshold setting. To overcome this limitation, this study proposed a dynamic Doppler multisource separation model and constructed a time domain-separating matrix (TDSM) to realize multiple moving sources separation in the time domain. The TDSM was designed with two steps of (1) constructing separating curves and time domain remapping matrix (TDRM) and (2) remapping each element of separating curves to its corresponding time according to the TDRM. Both TDSM and TDRM were driven by geometrical and motion parameters, which would be estimated by Doppler feature matching pursuit (DFMP) algorithm. After gaining the source components from the observed signals, correlation operation was carried out to estimate source signals. Moreover, fault diagnosis could be carried out by envelope spectrum analysis. Compared with the method of constructing TF masks, the proposed strategy could avoid setting thresholds empirically. Finally, the effectiveness of the proposed technique was validated by simulation and experimental cases. Results indicated the potential of this method for improving the performance of the ADBD system.

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.

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