An Underwater Sound Localization Technique Based on the Combination of Chaotic Electronic Signal and Sensor Array

2017 ◽  
Vol 12 (8) ◽  
pp. 825-831
Author(s):  
Xingcheng Han ◽  
Liming Wang
Keyword(s):  
Sound Localization ◽  
Sensor Array ◽  
Underwater Sound ◽  
Localization Technique ◽  
Electronic Signal

Effect of training on human underwater sound‐localization ability

1975 ◽  
Vol 57 (5) ◽  
pp. 1212-1213 ◽  
Author(s):  
James L. Stouffer ◽  
E. Thomas Doherty ◽  
Harry Hollien
Keyword(s):  
Sound Localization ◽  
Underwater Sound ◽  
Localization Ability

scholarly journals Configuration-Invariant Sound Localization Technique Using Azimuth-Frequency Representation and Convolutional Neural Networks

Sensors ◽  
2020 ◽  
Vol 20 (13) ◽  
pp. 3768
Author(s):  
Chanjun Chun ◽  
Kwang Myung Jeon ◽  
Wooyeol Choi
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Sound Localization ◽  
Speech Processing ◽  
Image Method ◽  
Frame Size ◽  
Localization Technique ◽  
Time Frequency ◽  
Input Size ◽  
Frequency Representation

Deep neural networks (DNNs) have achieved significant advancements in speech processing, and numerous types of DNN architectures have been proposed in the field of sound localization. When a DNN model is deployed for sound localization, a fixed input size is required. This is generally determined by the number of microphones, the fast Fourier transform size, and the frame size. if the numbers or configurations of the microphones change, the DNN model should be retrained because the size of the input features changes. in this paper, we propose a configuration-invariant sound localization technique using the azimuth-frequency representation and convolutional neural networks (CNNs). the proposed CNN model receives the azimuth-frequency representation instead of time-frequency features as the input features. the proposed model was evaluated in different environments from the microphone configuration in which it was originally trained. for evaluation, single sound source is simulated using the image method. Through the evaluations, it was confirmed that the localization performance was superior to the conventional steered response power phase transform (SRP-PHAT) and multiple signal classification (MUSIC) methods.

scholarly journals Subliminal Underwater Sound Localization

1973 ◽  
Vol 53 (1) ◽  
pp. 336-336 ◽  
Author(s):  
H. Hollien ◽  
J. Kinney ◽  
S. Feinstein ◽  
P. Hollien
Keyword(s):  
Sound Localization ◽  
Underwater Sound

Additional Data on Underwater Sound Localization

1970 ◽  
Vol 47 (1A) ◽  
pp. 127-128
Author(s):  
Harry Hollien ◽  
J. L. Lauer ◽  
Patricia Paul
Keyword(s):  
Sound Localization ◽  
Additional Data ◽  
Underwater Sound

Underwater sound localization: Role of interaural time and intensity differences

1998 ◽  
Vol 103 (5) ◽  
pp. 2844-2844 ◽  
Author(s):  
Pierre Bovet ◽  
Carolyn Drake ◽  
François Bernaschina ◽  
Sohpie Savel
Keyword(s):  
Sound Localization ◽  
Underwater Sound

Underwater sound localization of pure tones in the median plane by harbor seals (Phoca vitulina)

2016 ◽  
Vol 140 (6) ◽  
pp. 4490-4495 ◽  
Author(s):  
Jenny Ann Byl ◽  
Lars Miersch ◽  
Sven Wieskotten ◽  
Guido Dehnhardt
Keyword(s):  
Sound Localization ◽  
Median Plane ◽  
Phoca Vitulina ◽  
Harbor Seals ◽  
Underwater Sound ◽  
Pure Tones

Modeling the utility of binaural cues for underwater sound localization

2014 ◽  
Vol 312 ◽  
pp. 103-113 ◽  
Author(s):  
Jennifer N. Schneider ◽  
David R. Lloyd ◽  
Patchouly N. Banks ◽  
Eduardo Mercado
Keyword(s):  
Sound Localization ◽  
Underwater Sound ◽  
Binaural Cues
Sign in / Sign up

Export Citation Format

Share Document