Ocean Ambient Noise Classification Using Soft Techniques - OANCST

2012 ◽  
pp. 233-242
Author(s):  
V. Vaithiyanathan ◽  
R. D. Sathiya ◽  
G. VictorRajamanickam ◽  
G. Latha
Keyword(s):  
Ambient Noise ◽  
Noise Classification

Analysis and Modeling of Ambient Noise in Shallow Water of Arabian Sea

2012 ◽  
Vol 2 (6) ◽  
pp. 271-272
Author(s):  
Sudhir Pal Singh Rawat ◽  
◽  
Dr. Arnab Das ◽  
Dr. H.G.Virani Dr. H.G.Virani ◽  
Dr. Y.K.Somayajulu Dr. Y.K.Somayajulu
Keyword(s):  
Shallow Water ◽  
Arabian Sea ◽  
Ambient Noise

Particularities of tinnitus in children and adolescents

ORL ro ◽  
2016 ◽  
Vol 4 (1) ◽  
pp. 40-42
Author(s):  
Alexandra Boloș ◽  
Sebastian Cozma ◽  
Andreea Silvana Szalontay
Keyword(s):  
Quality Of Life ◽  
Young Adults ◽  
Children And Adolescents ◽  
Psychological Factors ◽  
Ambient Noise ◽  
Psychological Consequences ◽  
Therapeutic Approaches

Tinnitus is a common otologic symptom and probably the most troublesome. Tinnitus causes a number of physical and psychological consequences, that interfere with the quality of life of the patient. Many authors believe that the presence of tinnitus in children is a matter of lesser importance than in adults because it is met less frequently and would be a fleeting symptom, inoffensive for them (Graham, 1981). In addition, the prevalence of tinnitus during adolescence and even in young adults is increasing, possibly as a consequence of the increased ambient noise (Bulbul SF, Shargorodsky J). Various therapeutic approaches have generated different results, which led us to consider the role of psychological factors, hence the need to underline the particularities of this symptom in childhood.  

Squeak and rattle noise classification using radial basis function neural networks

2020 ◽  
Vol 68 (4) ◽  
pp. 283-293
Author(s):  
Oleksandr Pogorilyi ◽  
Mohammad Fard ◽  
John Davy ◽  
Mechanical and Automotive Engineering, School ◽  
Mechanical and Automotive Engineering, School ◽  
...  
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
High Accuracy ◽  
Training Method ◽  
Vehicle Interior ◽  
Trained Classifier ◽  
Different Types ◽  
Noise Classification ◽  
Automatic Tool ◽  
Multi Class Classification

In this article, an artificial neural network is proposed to classify short audio sequences of squeak and rattle (S&R) noises. The aim of the classification is to see how accurately the trained classifier can recognize different types of S&R sounds. Having a high accuracy model that can recognize audible S&R noises could help to build an automatic tool able to identify unpleasant vehicle interior sounds in a matter of seconds from a short audio recording of the sounds. In this article, the training method of the classifier is proposed, and the results show that the trained model can identify various classes of S&R noises: simple (binary clas- sification) and complex ones (multi class classification).

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