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 Analysis of effects of spherical microphone array physical parameters using simulations

2013 ◽  
Vol 26 (2) ◽  
pp. 107-119
Author(s):  
Dejan Ciric ◽  
Ana Djordjevic ◽  
Marko Licanin
Keyword(s):  
Sound Source ◽  
Microphone Array ◽  
Sound Field ◽  
Embedded Software ◽  
Microphone Arrays ◽  
Field Analysis ◽  
Physical Parameters ◽  
Spatial Sound ◽  
Spherical Microphone Arrays

Spherical microphone arrays are used for spatial sound field analysis. Although there are commercially available products, they are not the most suitable for research due to their price and working limits of the embedded software. In those cases it is more convenient to build an own prototype in a lab. In this paper, the analysis of the effects of the physical parameters of a spherical microphone array is presented. The observed parameters are radius of the sphere, distance from the sound source and distribution of the microphone elements points over the sphere. The obtained results provide useful inputs for building a spherical microphone array for the desired applications.

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.

High Amplitude Sound Propagation at Low Frequencies in a Flow Duct Lined With Resonators: A Time Domain Solution

1988 ◽  
Vol 110 (4) ◽  
pp. 545-551 ◽  
Author(s):  
A. Cummings ◽  
I.-J. Chang
Keyword(s):  
Time Domain ◽  
Internal Combustion Engines ◽  
Sound Propagation ◽  
Sound Field ◽  
High Amplitude ◽  
Combustion Engines ◽  
Low Frequencies ◽  
The Time Domain ◽  
Flow Duct ◽  
Good Agreement

A quasi one-dimensional analysis of sound transmission in a flow duct lined with an array of nonlinear resonators is described. The solution to the equations describing the sound field and the hydrodynamic flow in the neighborhood of the resonator orifices is performed numerically in the time domain, with the object of properly accounting for the nonlinear interaction between the acoustic field and the resonators. Experimental data are compared to numerical computations in the time domain and generally very good agreement is noted. The method described here may readily be extended for use in the design of exhaust mufflers for internal combustion engines.

scholarly journals Mechanical assessment of defects in welded joints: morphological classification and data augmentation

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Hugo Launay ◽  
François Willot ◽  
David Ryckelynck ◽  
Jacques Besson
Keyword(s):  
Welded Joints ◽  
Spherical Harmonic ◽  
Data Augmentation ◽  
Mechanical Response ◽  
Training Data ◽  
Distance Functions ◽  
Elastic Response ◽  
Reduction Techniques ◽  
Harmonic Decomposition ◽  
Spherical Harmonic Decomposition

AbstractWe develop a methodology for classifying defects based on their morphology and induced mechanical response. The proposed approach is fairly general and relies on morphological operators (Angulo and Meyer in 9th international symposium on mathematical morphology and its applications to signal and image processing, pp. 226-237, 2009) and spherical harmonic decomposition as a way to characterize the geometry of the pores, and on the Grassman distance evaluated on FFT-based computations (Willot in C. R., Méc. 343(3):232–245, 2015), for the predicted elastic response. We implement and detail our approach on a set of trapped gas pores observed in X-ray tomography of welded joints, that significantly alter the mechanical reliability of these materials (Lacourt et al. in Int. J. Numer. Methods Eng. 121(11):2581–2599, 2020). The space of morphological and mechanical responses is first partitioned into clusters using the “k-medoids” criterion and associated distance functions. Second, we use multiple-layer perceptron neural networks to associate a defect and corresponding morphological representation to its mechanical response. It is found that the method provides accurate mechanical predictions if the training data contains a sufficient number of defects representing each mechanical class. To do so, we supplement the original set of defects by data augmentation techniques. Artificially-generated pore shapes are obtained using the spherical harmonic decomposition and a singular value decomposition performed on the pores signed distance transform. We discuss possible applications of the present method, and how medoids and their associated mechanical response may be used to provide a natural basis for reduced-order models and hyper-reduction techniques, in which the mechanical effects of defects and structures are decorrelated (Ryckelynck et al. in C. R., Méc. 348(10–11):911–935, 2020).

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