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.

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.

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.

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.

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.

Locating and tracking sound sources on a horizontal axis wind turbine using a compact microphone array based on beamforming

2019 ◽  
Vol 146 ◽  
pp. 295-309 ◽  
Author(s):  
Cui Qing Zhang ◽  
Zhi Ying Gao ◽  
Yong Yan Chen ◽  
Yuan Jun Dai ◽  
Jian Wen Wang ◽  
...  
Keyword(s):  
Wind Turbine ◽  
Microphone Array ◽  
Horizontal Axis ◽  
Horizontal Axis Wind Turbine ◽  
Sound Sources

Model-based Head Orientation Estimation for Smart Devices

2021 ◽  
Vol 5 (3) ◽  
pp. 1-24
Author(s):  
Qiang Yang ◽  
Yuanqing Zheng
Keyword(s):  
Radiation Pattern ◽  
Microphone Array ◽  
Estimation Error ◽  
Microphone Arrays ◽  
Smart Devices ◽  
Head Orientation ◽  
Context Information ◽  
Orientation Estimation ◽  
Model Based ◽  
The Voice

Voice interaction is friendly and convenient for users. Smart devices such as Amazon Echo allow users to interact with them by voice commands and become increasingly popular in our daily life. In recent years, research works focus on using the microphone array built in smart devices to localize the user's position, which adds additional context information to voice commands. In contrast, few works explore the user's head orientation, which also contains useful context information. For example, when a user says, "turn on the light", the head orientation could infer which light the user is referring to. Existing model-based works require a large number of microphone arrays to form an array network, while machine learning-based approaches need laborious data collection and training workload. The high deployment/usage cost of these methods is unfriendly to users. In this paper, we propose HOE, a model-based system that enables Head Orientation Estimation for smart devices with only two microphone arrays, which requires a lower training overhead than previous approaches. HOE first estimates the user's head orientation candidates by measuring the voice energy radiation pattern. Then, the voice frequency radiation pattern is leveraged to obtain the final result. Real-world experiments are conducted, and the results show that HOE can achieve a median estimation error of 23 degrees. To the best of our knowledge, HOE is the first model-based attempt to estimate the head orientation by only two microphone arrays without the arduous data training overhead.

scholarly journals Feasibility of Discriminating UAV Propellers Noise from Distress Signals to Locate People in Enclosed Environments Using MEMS Microphone Arrays

Sensors ◽  
2020 ◽  
Vol 20 (3) ◽  
pp. 597
Author(s):  
Alberto Izquierdo ◽  
Lara del Val ◽  
Juan J. Villacorta ◽  
Weikun Zhen ◽  
Sebastian Scherer ◽  
...  
Keyword(s):  
Unmanned Aerial Vehicle ◽  
Micro Electro Mechanical System ◽  
Microphone Arrays ◽  
Heat Sources ◽  
Sound Sources ◽  
Mems Microphone ◽  
Aerial Vehicle ◽  
Acoustic Array ◽  
Alternative Techniques ◽  
The University

Detecting and finding people are complex tasks when visibility is reduced. This happens, for example, if a fire occurs. In these situations, heat sources and large amounts of smoke are generated. Under these circumstances, locating survivors using thermal or conventional cameras is not possible and it is necessary to use alternative techniques. The challenge of this work was to analyze if it is feasible the integration of an acoustic camera, developed at the University of Valladolid, on an unmanned aerial vehicle (UAV) to locate, by sound, people who are calling for help, in enclosed environments with reduced visibility. The acoustic array, based on MEMS (micro-electro-mechanical system) microphones, locates acoustic sources in space, and the UAV navigates autonomously by closed enclosures. This paper presents the first experimental results locating the angles of arrival of multiple sound sources, including the cries for help of a person, in an enclosed environment. The results are promising, as the system proves able to discriminate the noise generated by the propellers of the UAV, at the same time it identifies the angles of arrival of the direct sound signal and its first echoes reflected on the reflective surfaces.

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