scholarly journals Sound Pattern Recognition in Some North American Treefrogs (Anura: Hylidae): Implications for Mate Choice

1982 ◽  
Vol 22 (3) ◽  
pp. 581-595 ◽  
Author(s):  
H. CARL GERHARDT
Keyword(s):  
Pattern Recognition ◽  
Mate Choice ◽  
North American ◽  
Sound Pattern ◽  
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

scholarly journals Multimodal signalling in the North American barn swallow: a phenotype network approach

2015 ◽  
Vol 282 (1816) ◽  
pp. 20151574 ◽  
Author(s):  
Matthew R. Wilkins ◽  
Daizaburo Shizuka ◽  
Maxwell B. Joseph ◽  
Joanna K. Hubbard ◽  
Rebecca J. Safran
Keyword(s):  
Mate Choice ◽  
North American ◽  
Communication Systems ◽  
Animal Communication ◽  
Complex Signal ◽  
Network Approach ◽  
Barn Swallow ◽  
Complex Signals ◽  
The North ◽  
Phenotype Network

Complex signals, involving multiple components within and across modalities, are common in animal communication. However, decomposing complex signals into traits and their interactions remains a fundamental challenge for studies of phenotype evolution. We apply a novel phenotype network approach for studying complex signal evolution in the North American barn swallow ( Hirundo rustica erythrogaster ). We integrate model testing with correlation-based phenotype networks to infer the contributions of female mate choice and male–male competition to the evolution of barn swallow communication. Overall, the best predictors of mate choice were distinct from those for competition, while moderate functional overlap suggests males and females use some of the same traits to assess potential mates and rivals. We interpret model results in the context of a network of traits, and suggest this approach allows researchers a more nuanced view of trait clustering patterns that informs new hypotheses about the evolution of communication systems.

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