scholarly journals Comparison of LMS adaptive beamforming techniques in microphone arrays

2015 ◽  
Vol 12 (1) ◽  
pp. 1-16
Author(s):  
Milos Bjelic ◽  
Miodrag Stanojevic
Keyword(s):  
Experimental Testing ◽  
Microphone Array ◽  
Training Sequence ◽  
Adaptive Beamforming ◽  
Microphone Arrays ◽  
Lms Algorithm ◽  
Detailed Explanation ◽  
Measurement Results ◽  
Array Beamforming ◽  
Acoustical Signals

This paper discusses principles of microphone array beamforming, specifically the use of LMS algorithm with training sequence. The problem of wideband nature of acoustical signals and its impact on the techniques of beamforming are discussed. Detailed explanation of classic narrowband and wideband LMS beamformers is presented, as well as the modification of narrowband algorithm with pre-steering. Experimental testing and comparison of algorithm performances was conducted and measurement results are presented. The used microphone array is part of Br?el & Kj?r acoustical camera, and is comprised of 18 omnidirectional non-uniformly spaced microphones.

scholarly journals Classification, positioning, and tracking of drones by HMM using acoustic circular microphone array beamforming

2020 ◽  
Vol 2020 (1) ◽  
Author(s):  
Junfeng Guo ◽  
Ishtiaq Ahmad ◽  
KyungHi Chang
Keyword(s):  
Background Noise ◽  
Microphone Array ◽  
Reference Signal ◽  
Adaptive Beamforming ◽  
Commercial Application ◽  
Monitoring Systems ◽  
Focal Points ◽  
Sound Sources ◽  
Recorded Sound ◽  
Array Beamforming

AbstractThis paper addresses issues with monitoring systems that identify and track illegal drones. The development of drone technologies promotes the widespread commercial application of drones. However, the ability of a drone to carry explosives and other destructive materials may pose serious threats to public safety. In order to reduce these threats, we propose an acoustic-based scheme for positioning and tracking of illegal drones. Our proposed scheme has three main focal points. First, we scan the sky with switched beamforming to find sound sources and record the sounds using a microphone array; second, we perform classification with a hidden Markov model (HMM) in order to know whether the sound is a drone or something else. Finally, if the sound source is a drone, we use its recorded sound as a reference signal for tracking based on adaptive beamforming. Simulations are conducted under both ideal conditions (without background noise and interference sounds) and non-ideal conditions (with background noise and interference sounds), and we evaluate the performance when tracking illegal drones.

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.

scholarly journals Rotating equipment fault noise location based on adaptive beamforming algorithm

2021 ◽  
Vol 2113 (1) ◽  
pp. 012042
Author(s):  
Yongshao Xu ◽  
Bingzheng Liu ◽  
Haotian Shang ◽  
Mingduo Wang
Keyword(s):  
Microphone Array ◽  
State Parameter ◽  
Noise Signal ◽  
Source Location ◽  
Adaptive Beamforming ◽  
Rotating Machinery ◽  
Acoustic Sensor ◽  
Unsteady State ◽  
Beamforming Algorithm ◽  
Fault Source

Abstract Rotating machinery often produces continuous impact during operation due to the change of load and speed, which shows the characteristics of unsteady state and time-varying. Its working state can not be comprehensively judged by a single vibration state parameter. Therefore, this paper proposes to use acoustic sensors to collect the fault noise signal of rotating machinery, and use the whole column of sensors to detect the fault noise signal. Based on the microphone array, this paper studies the adaptive beamforming algorithm (MVDR) to locate the fault source of rotating machinery in space. The effect of fault source location is verified by simulation and equipment measurement experiments. The acoustic sensor does not in contact with the equipment, which will not damage the generator set, but also provide more effective information for fault source location and fault diagnosis and analysis.

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.

Spherical Microphone Array Beamforming

2010 ◽  
pp. 281-305 ◽  
Author(s):  
Boaz Rafaely ◽  
Yotam Peled ◽  
Morag Agmon ◽  
Dima Khaykin ◽  
Etan Fisher
Keyword(s):  
Microphone Array ◽  
Array Beamforming
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