Data completion method for the characterization of sound sources

2011 ◽  
Vol 130 (4) ◽  
pp. 2016-2023 ◽  
Author(s):  
Christophe Langrenne ◽  
Alexandre Garcia
Keyword(s):  
Sound Sources ◽  
Data Completion

scholarly journals Spectral Decomposition of the Flow and Characterization of the Sound Signals through Stenoses with Different Levels of Severity

2021 ◽  
Vol 8 (3) ◽  
pp. 41
Author(s):  
Fardin Khalili ◽  
Peshala T. Gamage ◽  
Amirtahà Taebi ◽  
Mark E. Johnson ◽  
Randal B. Roberts ◽  
...  
Keyword(s):  
Pressure Fluctuations ◽  
High Energy ◽  
Sound Sources ◽  
Severity Level ◽  
Non Invasive ◽  
Flow Through ◽  
Severity Of The Disease ◽  
Proper Orthogonal ◽  
Different Levels

Treatments of atherosclerosis depend on the severity of the disease at the diagnosis time. Non-invasive diagnosis techniques, capable of detecting stenosis at early stages, are essential to reduce associated costs and mortality rates. We used computational fluid dynamics and acoustics analysis to extensively investigate the sound sources arising from high-turbulent fluctuating flow through stenosis. The frequency spectral analysis and proper orthogonal decomposition unveiled the frequency contents of the fluctuations for different severities and decomposed the flow into several frequency bandwidths. Results showed that high-intensity turbulent pressure fluctuations appeared inside the stenosis for severities above 70%, concentrated at plaque surface, and immediately in the post-stenotic region. Analysis of these fluctuations with the progression of the stenosis indicated that (a) there was a distinct break frequency for each severity level, ranging from 40 to 230 Hz, (b) acoustic spatial-frequency maps demonstrated the variation of the frequency content with respect to the distance from the stenosis, and (c) high-energy, high-frequency fluctuations existed inside the stenosis only for severe cases. This information can be essential for predicting the severity level of progressive stenosis, comprehending the nature of the sound sources, and determining the location of the stenosis with respect to the point of measurements.

Interface Mobilities for Characterization of Structure-Borne Sound Sources with Multi-Point Coupling

2012 ◽  
Vol 98 (3) ◽  
pp. 384-391 ◽  
Author(s):  
H. A. Bonhoff ◽  
A. Eslami
Keyword(s):  
Single Point ◽  
Point Of View ◽  
Source Characterization ◽  
Sound Sources ◽  
Fourier Decomposition ◽  
Interface Mobility ◽  
Contact Points ◽  
Simplified Calculation ◽  
Set Of Points

The concept of source descriptor and coupling function is commonly recognized to form a rigorous basis for structure-borne sound source characterization. While this concept initially is valid for the single-point case only, it can be extended to sources with multi-point coupling by including the interface mobility approach. By considering a continuous interface that passes all contact points, the velocities, forces and mobilities are series expanded into interface orders by means of a spatial Fourier decomposition. The use of a continuous formulation for the multi-point case, however, can be problematic from a practical point of view. This paper discusses a reformulation of the interface mobility approach for a simplified calculation and clarified interpretation of the interface orders. With a discrete Fourier series as a basis for the interface mobility approach, the interface is reduced to a set of points and the interface orders are shown to describe the interplay of the data at the contact points. A discrete formulation furthermore yields simplified equations and a strict upper bound for the number of orders that have to be included, thus enhancing the practicability of interface mobilities for source characterization.

scholarly journals Machine Learning-Based Tools for Wind Turbine Acoustic Monitoring

2021 ◽  
Vol 11 (14) ◽  
pp. 6488
Author(s):  
Giuseppe Ciaburro ◽  
Gino Iannace ◽  
Virginia Puyana-Romero ◽  
Amelia Trematerra
Keyword(s):  
Wind Turbine ◽  
Simulation Models ◽  
Difficult Problem ◽  
Operating Conditions ◽  
Support Vector ◽  
Sound Sources ◽  
Model Based ◽  
Acoustic Characterization ◽  
Specific Frequency

The identification and separation of sound sources has always been a difficult problem for acoustic technicians to tackle. This is due to the considerable complexity of a sound that is made up of many contributions at different frequencies. Each sound has a specific frequency spectrum, but when many sounds overlap it becomes difficult to discriminate between the different contributions. In this case, it can be extremely useful to have a tool that is capable of identifying the operating conditions of an acoustic source. In this study, measurements were made of the noise emitted by a wind turbine in the vicinity of a sensitive receptor. To identify the operating conditions of the wind turbine, average spectral levels in one-third octave bands were used. A model based on a support vector machine (SVM) was developed for the detection of the operating conditions of the wind turbine; then a model based on an artificial neural network was used to compare the performance of both models. The high precision returned by the simulation models supports the adoption of these tools as a support for the acoustic characterization of noise in environments close to wind turbines.

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