The features of sound propagation through human lungs, revealed by transmission sounding with phase manipulated acoustic signal of 80–1000 Hz frequency band

2015 ◽  
Vol 137 (4) ◽  
pp. 2424-2424
Author(s):  
Vladimir Korenbaum ◽  
Anton Shiryaev
Keyword(s):  
Frequency Band ◽  
Acoustic Signal ◽  
Sound Propagation ◽  
Human Lungs

Influence of Internal Wave on the Sound Propagation in the Subsurface Bubble Layer

2011 ◽  
Vol 8 (1) ◽  
pp. 54-64
Author(s):  
R. Grimshaw ◽  
L.A. Ostrovsky ◽  
A.S. Topolnikov ◽  
K.R. Khusnutdinova
Keyword(s):  
Internal Wave ◽  
Speed Of Sound ◽  
Acoustic Signal ◽  
Sound Propagation ◽  
Japan Sea ◽  
Subsurface Ocean ◽  
Ocean Layer ◽  
Bubble Layer ◽  
Linear Internal Wave ◽  
Local Speed

In the paper the influence of non-linear internal wave on the propagation of acoustic signal in the subsurface ocean layer containing gas bubbles is considered. During interaction with surface waves the internal wave causes its collapse and influences the structure of bubble layer. Inhomogeneous structure of the layer promotes the local speed of sound and intensity of scattering near the ocean surface to modulate by internal wave with slight shift in phase in the direction of its propagation, which agree with recent experimental observations made on the shelf of Japan Sea.

scholarly journals Texture Classification Using Spectral Entropy of Acoustic Signal Generated by a Human Echolocator

Entropy ◽  
2019 ◽  
Vol 21 (10) ◽  
pp. 963
Author(s):  
Raja Abdullah ◽  
Saleh ◽  
Syed Abdul Rahman ◽  
Zamri ◽  
Rashid
Keyword(s):  
Acoustic Signal ◽  
Classification Scheme ◽  
Nearest Neighbor ◽  
Sound Propagation ◽  
Texture Classification ◽  
Synthetic Data ◽  
Spectral Entropy ◽  
K Nearest Neighbor ◽  
Echo Signal ◽  
Classifier Performance

Human echolocation is a biological process wherein the human emits a punctuated acoustic signal, and the ear analyzes the echo in order to perceive the surroundings. The peculiar acoustic signal is normally produced by clicking inside the mouth. This paper utilized this unique acoustic signal from a human echolocator as a source of transmitted signal in a synthetic human echolocation technique. Thus, the aim of the paper was to extract information from the echo signal and develop a classification scheme to identify signals reflected from different textures at various distance. The scheme was based on spectral entropy extracted from Mel-scale filtering output in the Mel-frequency cepstrum coefficient of a reflected echo signal. The classification process involved data mining, features extraction, clustering, and classifier validation. The reflected echo signals were obtained via an experimental setup resembling a human echolocation scenario, configured for synthetic data collection. Unlike in typical speech signals, extracted entropy from the formant characteristics was likely not visible for the human mouth-click signals. Instead, multiple peak spectral features derived from the synthesis signal of the mouth-click were assumed as the entropy obtained from the Mel-scale filtering output. To realize the classification process, K-means clustering and K-nearest neighbor processes were employed. Moreover, the impacts of sound propagation toward the extracted spectral entropy used in the classification outcome were also investigated. The outcomes of the classifier performance herein indicated that spectral entropy is essential for human echolocation.

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