Hearing loss from interrupted, intermittent, and time varying Gaussian noise exposures: The applicability of the equal energy hypothesis

2007 ◽  
Vol 121 (3) ◽  
pp. 1613-1620 ◽  
Author(s):  
Wei Qiu ◽  
Bob Davis ◽  
Roger P. Hamernik
Keyword(s):  
Hearing Loss ◽  
Gaussian Noise ◽  
Time Varying ◽  
Equal Energy

scholarly journals Gaussian Noise Time-Varying Power Spectrum Estimation With Minimal Statistics

2014 ◽  
Vol 62 (22) ◽  
pp. 5892-5906 ◽  
Author(s):  
Julien Huillery ◽  
Fabien Millioz ◽  
Nadine Martin
Keyword(s):  
Power Spectrum ◽  
Gaussian Noise ◽  
Spectrum Estimation ◽  
Time Varying ◽  
Power Spectrum Estimation

scholarly journals Generalized Automatic Modulation Classification Under Non-Gaussian Noise with Varying SNR Conditions: A CNN Enable Method

2020 ◽  
Author(s):  
Yu Wang ◽  
Guan Gui ◽  
Tomoaki Ohtsuki ◽  
Fumiyuki Adachi
Keyword(s):  
Gaussian Noise ◽  
Signal To Noise Ratio ◽  
Modulation Classification ◽  
Time Varying ◽  
Signal Modulation ◽  
Generalization Capability ◽  
Common Features ◽  
Automatic Modulation Classification ◽  
Simulation Results ◽  
Non Gaussian

Automatic modulation classification (AMC) is an critical step to identify signal modulation types so as to enable more accurate demodulation in the non-cooperative scenarios. Convolutional neural network (CNN)-based AMC is believed as one of the most promising methods with great classification accuracy. However, the conventional CNN-based methods are lack of generality capabilities under time-varying signal-to-noise ratio (SNR) conditions, because these methods are merely trained on specific datasets and can only work at the corresponding condition. In this paper, a novel CNN-based generalized AMC method is proposed, and a more realistic scenario is considered, including white non-Gaussian noise and synchronization error. Its generalization capability stems from the mixed datasets under varying noise scenarios, and the CNN can extract common features from these datasets. Simulation results show that our proposed architecture can achieve higher robustness and generalization than the conventional ones.

Noise and Public Health

2011 ◽  
Vol 12 (1) ◽  
pp. 19-23
Author(s):  
Ann Dix
Keyword(s):  
Public Health ◽  
Hearing Loss ◽  
Health Effects ◽  
Background Information ◽  
Regulatory Agencies ◽  
Loss Prevention ◽  
Energy Principle ◽  
Recreational Activities ◽  
Equal Energy ◽  
Workplace Noise

In this article, I provide an overview of current issues and resources related to noise and public health for speech and hearing professionals and their clients. For context, some background information is given, including a brief description of the equal energy principle and the measurement of sound. Regulatory agencies and guidelines for recreational versus workplace noise are differentiated. Some common noisy recreational activities are surveyed and resources for education about hearing loss prevention are listed. Auditory as well as non-auditory health effects of noise are described and explored.

Gearbox Fault Identification Under Non-Gaussian Noise and Time-Varying Operating Conditions

2021 ◽  
pp. 1-9
Author(s):  
Stephan Schmidt ◽  
Fakher Chaari ◽  
Radoslaw Zimroz ◽  
P. Stephan Heyns ◽  
Mohamed Haddar
Keyword(s):  
Gaussian Noise ◽  
Operating Conditions ◽  
Fault Identification ◽  
Time Varying ◽  
Non Gaussian

scholarly journals Occupational Hearing Loss from Non-Gaussian Noise

2017 ◽  
Vol 38 (03) ◽  
pp. 223-224
Author(s):  
Mark Stephenson ◽  
Keyword(s):  
Hearing Loss ◽  
Gaussian Noise ◽  
Non Gaussian ◽  
Occupational Hearing Loss

scholarly journals Occupational Hearing Loss Associated With Non-Gaussian Noise

2021 ◽  
Vol Publish Ahead of Print ◽  
Author(s):  
Zhihao Shi ◽  
Jiena Zhou ◽  
Yuwen Huang ◽  
Yong Hu ◽  
Lifang Zhou ◽  
...  
Keyword(s):  
Hearing Loss ◽  
Gaussian Noise ◽  
Non Gaussian ◽  
Occupational Hearing Loss
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