Geoacoustic inversions using Combustive Sound Source data from Shallow Water-2006 experiment

2009 ◽  
Author(s):  
Gopu R. Potty ◽  
James H. Miller
Keyword(s):  
Shallow Water ◽  
Sound Source ◽  
Source Data

Shallow water sound source localization using the iterative beamforming method in an image framework

2017 ◽  
Vol 395 ◽  
pp. 354-370 ◽  
Author(s):  
Xun Wang ◽  
Shahram Khazaie ◽  
Luca Margheri ◽  
Pierre Sagaut
Keyword(s):  
Shallow Water ◽  
Source Localization ◽  
Sound Source ◽  
Sound Source Localization

Interferometric method for estimating the velocity of a noise sound source and the distance to it in shallow water using a vector-scalar receiver

2017 ◽  
Vol 25 (4) ◽  
pp. 299-306 ◽  
Author(s):  
G. N. Kuznetsov ◽  
V. M. Kuz’kin ◽  
S. A. Pereselkov ◽  
I. V. Kaznacheev ◽  
V. A. Grigor’ev
Keyword(s):  
Shallow Water ◽  
Sound Source ◽  
Interferometric Method

scholarly journals Inferring Association Between Alcohol Addiction and Defendant's Emotion Based on Sound at Court

2021 ◽  
Vol 12 ◽  
Author(s):  
Yun Song ◽  
Zhongyu Wei
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Emotion Recognition ◽  
Sound Source ◽  
Alcohol Addiction ◽  
Sound Perception ◽  
Learning Framework ◽  
Proposed Model ◽  
Source Data ◽  
Better Than

Alcohol addiction can lead to health and social problems. It can also affect people's emotions. Emotion plays a key role in human communications. It is important to recognize the people's emotions at the court and infer the association between the people's emotions and the alcohol addiction. However, it is challenging to recognize people's emotions efficiently in the courtroom. Furthermore, to the best of our knowledge, no existing work is about the association between alcohol addiction and people's emotions at court. In this paper, we propose a deep learning framework for predicting people's emotions based on sound perception, named ResCNN-SER. The proposed model combines several neural network-based components to extract the features of the speech signals and predict the emotions. The evaluation shows that the proposed model performs better than existing methods. By applying ResCNN-SER for emotion recognition based on people's voices at court, we infer the association between alcohol addiction and the defendant's emotion at court. Based on the sound source data from 54 trial records, we found that the defendants with alcohol addiction tend to get angry or fearful more easily at court comparing with defendants without alcohol addiction.

scholarly journals On the Dispersive Characteristics of Shallow Water Seismic Waves Excited by Air Gun Sound Source in Shallow Water

2019 ◽  
Vol 37 (4) ◽  
pp. 724-729 ◽  
Author(s):  
Aimin Diao ◽  
Guangli Cheng ◽  
Zeming Wang
Keyword(s):  
Shallow Water ◽  
Sound Source ◽  
Seismic Waves ◽  
Sound Field ◽  
Staggered Grid ◽  
Dispersion Characteristics ◽  
Solid Interface ◽  
Air Gun ◽  
Scholte Waves ◽  
Scholte Wave

In order to study the dispersion characteristics of seabed seismic waves excited by acoustic sources in shallow water, the sound field at liquid-solid interface is derived based on the wave equation firstly, which results demonstrate that the longitudinal wave and transversal wave are not dispersive, while the Scholte wave is normal dispersive. Then, the numerical simulation is carried out on the sound field at the liquid-solid interface excited by the air gun sound source based on high-order staggered grid finite difference method, which results demonstrate that the dispersion phenomenon of Scholte waves is observed and the theoretical analysis results were verified. Finally, the data of experiment in the sea are analyzed based on the transformation, and the dispersion curve of Scholte wave is finely extracted.

Sign in / Sign up

Export Citation Format

Share Document