A gating mechanism for sound pattern recognition is correlated with the temporal structure of echolocation sounds in the rufous horseshoe bat

1989 ◽  
Vol 166 (2) ◽  
Author(s):  
RoaldC. Roverud
Keyword(s):  
Pattern Recognition ◽  
Temporal Structure ◽  
Sound Pattern ◽  
Gating Mechanism ◽  
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

scholarly journals Pattern Recognition Bird Sounds Based on Their Type Using Discreate Cosine Transform (DCT) and Gaussian Methods

2019 ◽  
pp. 233-240
Author(s):  
Hendro Nugroho ◽  
Wahyu Widodo ◽  
Andy Rachman
Keyword(s):  
Pattern Recognition ◽  
Discrete Cosine Transform ◽  
Bird Species ◽  
Graphical Form ◽  
Cosine Transform ◽  
Sound Pattern ◽  
Sound Duration

To know the type of bird, most people know from the shape of bird species and the sound of birds. In this study, it identified the pattern of bird sounds. The bird sounds studied were Canary Trills, Vulture and Crow birds. In the introduction of the type of bird sound pattern in this study using the Discrete Cosine Transform (DCT) method and Gaussian value. The researcher conducted several steps to get the sound model of birds, among others, namely (1) bird sound input in the form of WAV file, (2) Hamming Windowing, (3) DFT / FFT, (4) Mel Bank Filter, (5) DCT, and (6) Value Gaussian. The output obtained is in the form of vector values and represented in graphical form. The results obtained in the study of pattern recognition of bird sound types get the results of observations in the same bird sound duration and frequency of the same, then the same pattern is obtained in the same bird as evidenced by calculating the closest distance value with Bray Curtis method. For the same duration of time and the length of the frequency that is not the same; it found that the pattern of bird sounds is not the same.

Software pattern recognition applied to nanodiffraction patterns from a STEM instrument

1986 ◽  
Vol 44 ◽  
pp. 694-695
Author(s):  
G.Y. Fan ◽  
J.M. Cowley
Keyword(s):  
Image Processing ◽  
Pattern Recognition ◽  
Computer Software ◽  
Processing System ◽  
Light Level ◽  
Host Computer ◽  
Low Light ◽  
Image Processing System ◽  
Recent Developments ◽  
On Line

In recent developments, the ASU HB5 has been modified so that the timing, positioning, and scanning of the finely focused electron probe can be entirely controlled by a host computer. This made the asynchronized handshake possible between the HB5 STEM and the image processing system which consists of host computer (PDP 11/34), DeAnza image processor (IP 5000) which is interfaced with a low-light level TV camera, array processor (AP 400) and various peripheral devices. This greatly facilitates the pattern recognition technique initiated by Monosmith and Cowley. Software called NANHB5 is under development which, instead of employing a set of photo-diodes to detect strong spots on a TV screen, uses various software techniques including on-line fast Fourier transform (FFT) to recognize patterns of greater complexity, taking advantage of the sophistication of our image processing system and the flexibility of computer software.

Sign in / Sign up

Export Citation Format

Share Document