scholarly journals Robust Multichannel Microphone Beamforming

2021 ◽  
Author(s):  
◽  
Craig Anderson
Keyword(s):  
Blind Source Separation ◽  
Correlation Functions ◽  
Microphone Array ◽  
Near Field ◽  
Source Separation ◽  
Source Location ◽  
Mobile Platforms ◽  
Signal Path ◽  
Von Mises ◽  
2D And 3D

<p>In this thesis, a method for the design and implementation of a spatially robust multichannel microphone beamforming system is presented.  A set of spatial correlation functions are derived for 2D and 3D far-field/near-field scenarios based on von Mises(-Fisher), Gaussian, and uniform source location distributions. These correlation functions are used to design spatially robust beamformers and blocking beamformers (nullformers) designed to enhance or suppress a known source, where the target source location is not perfectly known due to either an incorrect location estimate or movement of the target while the beamformers are active.  The spatially robust beam/null-formers form signal and interferer plus noise references which can be further processed via a blind source separation algorithm to remove mutual components - removing the interference and sensor noise from the signal path and vice versa. The noise reduction performance of the combined beamforming and blind source separation system approaches that of a perfect information MVDR beamformer under reverberant conditions.  It is demonstrated that the proposed algorithm can be implemented on low-power hardware with good performance on hardware similar to current mobile platforms using a four-element microphone array.</p>

scholarly journals Robust Multichannel Microphone Beamforming

2021 ◽  
Author(s):  
◽  
Craig Anderson
Keyword(s):  
Blind Source Separation ◽  
Correlation Functions ◽  
Microphone Array ◽  
Near Field ◽  
Source Separation ◽  
Source Location ◽  
Mobile Platforms ◽  
Signal Path ◽  
Von Mises ◽  
2D And 3D

<p>In this thesis, a method for the design and implementation of a spatially robust multichannel microphone beamforming system is presented.  A set of spatial correlation functions are derived for 2D and 3D far-field/near-field scenarios based on von Mises(-Fisher), Gaussian, and uniform source location distributions. These correlation functions are used to design spatially robust beamformers and blocking beamformers (nullformers) designed to enhance or suppress a known source, where the target source location is not perfectly known due to either an incorrect location estimate or movement of the target while the beamformers are active.  The spatially robust beam/null-formers form signal and interferer plus noise references which can be further processed via a blind source separation algorithm to remove mutual components - removing the interference and sensor noise from the signal path and vice versa. The noise reduction performance of the combined beamforming and blind source separation system approaches that of a perfect information MVDR beamformer under reverberant conditions.  It is demonstrated that the proposed algorithm can be implemented on low-power hardware with good performance on hardware similar to current mobile platforms using a four-element microphone array.</p>

Acoustical feature extraction of rotating machinery with combined wave superposition and blind source separation

2006 ◽  
Vol 220 (9) ◽  
pp. 1423-1431 ◽  
Author(s):  
W F Xue ◽  
J Chen ◽  
J Q Li ◽  
X F Liu
Keyword(s):  
Fault Diagnosis ◽  
Blind Source Separation ◽  
Acoustic Radiation ◽  
Microphone Array ◽  
Source Separation ◽  
Point Sources ◽  
Superposition Method ◽  
Sound Sources ◽  
Reconstruction Process ◽  
Wave Superposition

As the result of vibration emission in air, machine sound signal carries affluent information about the working condition of machine and it can be used to make mechanical fault diagnosis. The fundamental problems with fault diagnosis are the estimation of the number of sound sources and the localization of sound sources. The wave superposition can be employed to identify and locate sound sources, which is based on the idea that an acoustic radiator can be approximated and represented by the sum of the fields due to a finite number of interior point sources. But, in practice, a large number of measurements must be used in order to achieve a desired resolution, which makes the reconstruction process very time-consuming and expensive. In this paper, a combined wave superposition method has been developed reconstruct to acoustic radiation from machine acoustical signals. This method combines the advantages of both the wave superposition and Helmholtz equationleast squares methods, and it allows for reconstruction of the acoustic field from an arbitrary object with relatively few measurements, thus significantly enhancing the reconstruction efficiency. After sound source localization, the blind source separation (BSS) is proposed to extract acoustical feature from the mixed measuring sound signals. In a semi-anechoic chamber, a cross-planar microphone array, which consists of 29 microphones, was successfully applied to obtain the two-dimensional mapping of the sound sources. The location, the sound pressure, and the properties in frequency domain of the sound sources can be found through this method precisely. The experimental results demonstrate that the methods presented can potentially become an acoustical diagnosis tool.

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