Harmonic and frequency structure used for echolocation sound pattern recognition and distance information processing in the rufous horseshoe bat

1989 ◽  
Vol 166 (2) ◽  
Author(s):  
RoaldC. Roverud
Keyword(s):  
Pattern Recognition ◽  
Information Processing ◽  
Distance Information ◽  
Sound Pattern ◽  
Frequency Structure ◽  
Horseshoe Bat ◽  
Sound Pattern Recognition

scholarly journals Analisis Bentuk Pola Suara Menggunakan Ekstraksi Ciri Mel-Frequencey Cepstral Coefficients (MFCC)

2019 ◽  
Vol 4 (2) ◽  
pp. 294 ◽  
Author(s):  
Rusydi Umar ◽  
Imam Riadi ◽  
Abdullah Hanif
Keyword(s):  
Pattern Recognition ◽  
Feature Extraction ◽  
Human Body ◽  
Extraction Process ◽  
Graphical Form ◽  
Voice Input ◽  
Form Analysis ◽  
Sound Pattern ◽  
Analysis And Design ◽  
Sound Pattern Recognition

Sound is a part of the human body that is unique and can be distinguished, so its application can be used in sound pattern recognition technology, one of which is used for sound biometrics. This study discusses the analysis of the form of a sound pattern that aims to determine the shape of the sound pattern of a person's character based on the spoken voice input. This study discusses the analysis of the form of a sound pattern that aims to determine the shape of the sound pattern of a person's character based on the spoken voice input. This study uses the Melf-Frequency Cepstrum Coefficients (MFCC) method for feature extraction process from speaker speech signals. The MFCC process will convert the sound signal into several feature vectors which will then be displayed in graphical form. Analysis and design of sound patterns using Matlab 2017a software. Tests were carried out on 5 users consisting of 3 men and 2 women, each user said 1 predetermined "LOGIN" word, which for 15 words said. The results of the test are the form of a sound pattern between the characteristics of 1 user with other users. Keywords—Voice, Pattern, Feature Extraction, MFCC

Sound pattern recognition supports automatic inspection

Sensor Review ◽  
1985 ◽  
Vol 5 (1) ◽  
pp. 13-17 ◽  
Author(s):  
Karl Beuter ◽  
Rainer Weiß
Keyword(s):  
Pattern Recognition ◽  
Automatic Inspection ◽  
Sound Pattern ◽  
Sound Pattern Recognition

Sound Pattern Recognition Using Optical Methods

1977 ◽  
Vol 24 (8) ◽  
pp. 811-825 ◽  
Author(s):  
Fanny Viénot ◽  
Claudine Bainier ◽  
Bernard Carquille ◽  
Michel Guignard
Keyword(s):  
Pattern Recognition ◽  
Optical Methods ◽  
Sound Pattern ◽  
Sound Pattern Recognition

Neural basis of sound pattern recognition in anurans

1990 ◽  
Vol 34 (4) ◽  
pp. 313-329 ◽  
Author(s):  
A FENG ◽  
J HALL ◽  
D GOOLER
Keyword(s):  
Pattern Recognition ◽  
Neural Basis ◽  
Sound Pattern ◽  
Sound Pattern Recognition

From Pattern Recognition to Economic Disequilibrium

2019 ◽  
Vol 51 (S1) ◽  
pp. 180-203
Author(s):  
Olessia Kirtchik
Keyword(s):  
Pattern Recognition ◽  
Information Processing ◽  
Autonomous Agents ◽  
Soviet Economy ◽  
Optimal Planning ◽  
Analytic Framework ◽  
Mathematical Economics ◽  
Third Way ◽  
Fixed Price ◽  
Styles Of Reasoning

This article is focused on the economic works of the Soviet machinelearning pioneer Emmanuil Braverman, who published, during the 1970s, a series of papers introducing disequilibrium fixed-price models of the Soviet economy. This highly original theory, developed independently from the Western analyses of disequilibria, proposed rationing mechanisms capable, under some conditions, of bringing a system to the state of equilibrium. However, in a fixed-price economy, equilibria are not necessarily optimal or effective; therefore specific observational and analytic procedures aiming at bringing a system to a better state had to be invented. Braverman interpreted this analytic framework as a “qualitative system of control” of the Soviet economy representing a sort of a third-way solution between neoclassical models of spontaneous coordination of autonomous agents and theories of optimal planning. This innovative approach, very different from the styles of reasoning in mathematical economics of his time, was grounded in his work on pattern recognition and informed by a cybernetic vision of control as information processing and communication in complex systems.

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