The Integration of Parallel Processing Streams in the Sound Localization System of the Barn Owl

1997 ◽  
pp. 735-739 ◽  
Author(s):  
James A. Mazer
Keyword(s):  
Parallel Processing ◽  
Sound Localization ◽  
Barn Owl ◽  
Localization System

scholarly journals A Bio-inspired Sound-localization System for a Mobile Agent

2006 ◽  
Vol 78 (9) ◽  
pp. 1435-1435
Author(s):  
H. Wagner ◽  
L. Calmes ◽  
G. Lakemeyer
Keyword(s):  
Sound Localization ◽  
Mobile Agent ◽  
Localization System

Binding Mechanism of Frequency-Specific ITD and IID Information in Sound Localization of Barn Owl

2006 ◽  
pp. 255-262
Author(s):  
Hidetaka Morisawa ◽  
Daisuke Hirayama ◽  
Kazuhisa Fujita ◽  
Yoshiki Kashimori ◽  
Takeshi Kambara
Keyword(s):  
Sound Localization ◽  
Barn Owl ◽  
Binding Mechanism

scholarly journals Monaural occlusion alters sound localization during a sensitive period in the barn owl

1984 ◽  
Vol 4 (4) ◽  
pp. 1001-1011 ◽  
Author(s):  
EI Knudsen ◽  
SD Esterly ◽  
PF Knudsen
Keyword(s):  
Sound Localization ◽  
Barn Owl ◽  
Sensitive Period

scholarly journals Parametric Estimations Based on Homomorphic Deconvolution for Time of Flight in Sound Source Localization System

Sensors ◽  
2020 ◽  
Vol 20 (3) ◽  
pp. 925 ◽  
Author(s):  
Yeonseok Park ◽  
Anthony Choi ◽  
Keonwook Kim
Keyword(s):  
Sound Localization ◽  
Source Localization ◽  
Sound Source ◽  
Average Length ◽  
Time Estimation ◽  
Flight Time ◽  
Ensemble Average ◽  
Sound Source Localization ◽  
Localization System ◽  
Non Parametric

Vehicle-mounted sound source localization systems provide comprehensive information to improve driving conditions by monitoring the surroundings. The three-dimensional structure of vehicles hinders the omnidirectional sound localization system because of the long and uneven propagation. In the received signal, the flight times between microphones delivers the essential information to locate the sound source. This paper proposes a novel method to design a sound localization system based on the single analog microphone network. This article involves the flight time estimation for two microphones with non-parametric homomorphic deconvolution. The parametric methods are also suggested with Yule-walker, Prony, and Steiglitz-McBride algorithm to derive the coefficient values of the propagation model for flight time estimation. The non-parametric and Steiglitz-McBride method demonstrated significantly low bias and variance for 20 or higher ensemble average length. The Yule-walker and Prony algorithms showed gradually improved statistical performance for increased ensemble average length. Hence, the non-parametric and parametric homomorphic deconvolution well represent the flight time information. The derived non-parametric and parametric output with distinct length will serve as the featured information for a complete localization system based on machine learning or deep learning in future works.

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