A FICTITIOUS SOURCE METHOD FOR A MULTIFREQUENCY ACOUSTIC SOURCE OVER GROUND WITH VARIABLE IMPEDANCE

2019 ◽  
Vol 17 (6) ◽  
pp. 563-582
Author(s):  
Y. Kamoun ◽  
Dan Givoli
Keyword(s):  
Acoustic Source ◽  
Source Method

scholarly journals Iteratively Reweighted Spherical Equivalent Source Method for Acoustic Source Identification

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 51513-51521
Author(s):  
Guoli Ping ◽  
Zhigang Chu ◽  
Yang Yang ◽  
Xu Chen
Keyword(s):  
Source Identification ◽  
Spherical Equivalent ◽  
Acoustic Source ◽  
Equivalent Source ◽  
Source Method ◽  
Equivalent Source Method

A Short Survey on Green’s Function for Acoustic Problems

2020 ◽  
Vol 28 (02) ◽  
pp. 1950025
Author(s):  
Augustus R. Okoyenta ◽  
Haijun Wu ◽  
Xueliang Liu ◽  
Weikang Jiang
Keyword(s):  
Integral Equation ◽  
Boundary Conditions ◽  
Green’S Function ◽  
Green's Function ◽  
Green's Functions ◽  
Green’S Functions ◽  
Acoustic Medium ◽  
Acoustic Source ◽  
Impedance Boundary ◽  
Source Method

Green’s functions for acoustic problems is the fundamental solution to the inhomogeneous Helmholtz equation for a point source, which satisfies specific boundary conditions. It is very significant for the integral equation and also serves as the impulse response of an acoustic wave equation. They are important for acoustic problems that involve the propagation of sound from the source point to the observer position. Once the Green’s function, which satisfies the necessary boundary conditions, is obtained, the sound pressure at any point away from the source can be easily calculated by the integral equation. The major problem faced by researchers is in the process of constructing these Green’s functions which satisfy a specific boundary condition. The aim of this work is to review some of these fundamental solutions available in the literature for different boundary conditions for the ease of analyzing acoustics problems. The review covers the free-space Green’s functions for stationary source and rotational source, for both when the observer and the acoustic medium are at rest and when the medium is in uniform flow. The half-space Green’s functions are also summarized for both stationary acoustic source and moving acoustic source, derived using the image source method, equivalent source method and complex equivalent method in both time domain and frequency domain. Each of these methods used depends on the different impedance boundary conditions for which the Green’s function will satisfy. Finally, enclosed spaced Green’s functions for both rectangular duct and cylindrical duct for an infinite and finite duct is also covered in the review.

scholarly journals Multichannel acoustic source and image dataset for the cocktail party effect in hearing aid and implant users

2020 ◽  
Vol 7 (1) ◽  
Author(s):  
Tim Fischer ◽  
Marco Caversaccio ◽  
Wilhelm Wimmer
Keyword(s):  
Hearing Aids ◽  
Scientific Community ◽  
Cocktail Party ◽  
Acoustic Source ◽  
Image Dataset ◽  
Cocktail Party Effect ◽  
Spatial Coordinates ◽  
Acoustic Chamber ◽  
Human Hearing ◽  
Human Sense

AbstractThe Cocktail Party Effect refers to the ability of the human sense of hearing to extract a specific target sound source from a mixture of background noises in complex acoustic scenarios. The ease with which normal hearing people perform this challenging task is in stark contrast to the difficulties that hearing-impaired subjects face in these situations. To help patients with hearing aids and implants, scientists are trying to imitate this ability of human hearing, with modest success so far. To support the scientific community in its efforts, we provide the Bern Cocktail Party (BCP) dataset consisting of 55938 Cocktail Party scenarios recorded from 20 people and a head and torso simulator wearing cochlear implant audio processors. The data were collected in an acoustic chamber with 16 synchronized microphones placed at purposeful positions on the participants’ heads. In addition to the multi-channel audio source and image recordings, the spatial coordinates of the microphone positions were digitized for each participant. Python scripts were provided to facilitate data processing.

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