Pitch Synchronous Analysis Method and Fisher Criterion Based Speaker Identification

2007 ◽  
Author(s):  
Yumin Zeng ◽  
Huayu Wu ◽  
Rong Gao
Keyword(s):  
Speaker Identification ◽  
Analysis Method ◽  
Fisher Criterion ◽  
Pitch Synchronous Analysis

scholarly journals Nonlinear Fault Separation for Redundancy Process Variables Based on FNN in MKFDA Subspace

2014 ◽  
Vol 2014 ◽  
pp. 1-9 ◽  
Author(s):  
Ying-ying Su ◽  
Shan Liang ◽  
Jing-zhe Li ◽  
Xiao-gang Deng ◽  
Tai-fu Li ◽  
...  
Keyword(s):  
Nearest Neighbors ◽  
Nonlinear Process ◽  
Process Industry ◽  
Analysis Method ◽  
Process Variables ◽  
Fisher Criterion ◽  
Tennessee Eastman Process ◽  
Original Process ◽  
Study Results ◽  
False Nearest Neighbors

Nonlinear faults are difficultly separated for amounts of redundancy process variables in process industry. This paper introduces an improved kernel fisher distinguish analysis method (KFDA). All the original process variables with faults are firstly optimally classified in multi-KFDA (MKFDA) subspace to obtain fisher criterion values. Multikernel is used to consider different distributions for variables. Then each variable is eliminated once from original sets, and new projection is computed with the same MKFDA direction. From this, differences between new Fisher criterion values and the original ones are tested. If it changed obviously, the effect of eliminated variable should be much important on faults called false nearest neighbors (FNN). The same test is applied to the remaining variables in turn. Two nonlinear faults crossed in Tennessee Eastman process are separated with lower observation variables for further study. Results show that the method in the paper can eliminate redundant and irrelevant nonlinear process variables as well as enhancing the accuracy of classification.

scholarly journals Method of Ear Detection for Maize Seed Based on Fisher Criterion

2018 ◽  
Vol 36 (2) ◽  
pp. 332-338
Author(s):  
Zhiqiang Kang ◽  
Chaohui Yuan ◽  
Ruifeng Cheng
Keyword(s):  
Discriminant Analysis ◽  
High Efficiency ◽  
Least Square ◽  
Analysis Method ◽  
Maize Seed ◽  
Fisher Criterion ◽  
Error Matrix ◽  
Linear Discriminant ◽  
Ear Detection ◽  
Discriminant Analysis Method

The purpose of this paper is to use machine vision to detect the size, shape, texture and color of maize seed ear. The traditional method is very limited and cannot meet the detection efficiency. This paper used logistic regression linear discriminant analysis method, the rule of perceptron, Fisher method and the least-square method, this paper use change between the Fisher criterion according to the the class of discrete degree error matrix and matrix of the discrete degree of reconstruction error in class than the maximum projection direction, classifying clusters. By fusion Fisher discriminant analysis method for testing in the test, through a large number of experiments and comparison method, the experiment proved that Fisher can high efficiency and high precision of classifying seed corn ear detection.

Impact of analysis method on anthocyanin content of elderberry products

Planta Medica ◽  
2007 ◽  
Vol 73 (09) ◽  
Author(s):  
C Chrubasik ◽  
T Maier ◽  
M Luond ◽  
A Schieber
Keyword(s):  
Anthocyanin Content ◽  
Analysis Method

An Analysis Method of Rollover Stability for Heavy Trucks

CICTP 2020 ◽  
2020 ◽  
Author(s):  
Hao Zhang ◽  
Yue Li ◽  
Cheng-Qiang Zong ◽  
Chuan-Jin Ou ◽  
Bing-Tao Li
Keyword(s):  
Analysis Method ◽  
Heavy Trucks ◽  
Rollover Stability

Method for measuring distortions of a speech signal during its transmission over a communication channel to a biometric identification system

2020 ◽  
pp. 65-72
Author(s):  
V. V. Savchenko ◽  
A. V. Savchenko
Keyword(s):  
Speech Signal ◽  
Communication Channel ◽  
Speaker Identification ◽  
A Priori ◽  
Small Samples ◽  
Practical Implementation ◽  
Identification System ◽  
New Information ◽  
Discrimination Criterion ◽  
A Priori Uncertainty

This paper is devoted to the presence of distortions in a speech signal transmitted over a communication channel to a biometric system during voice-based remote identification. We propose to preliminary correct the frequency spectrum of the received signal based on the pre-distortion principle. Taking into account a priori uncertainty, a new information indicator of speech signal distortions and a method for measuring it in conditions of small samples of observations are proposed. An example of fast practical implementation of the method based on a parametric spectral analysis algorithm is considered. Experimental results of our approach are provided for three different versions of communication channel. It is shown that the usage of the proposed method makes it possible to transform the initially distorted speech signal into compliance on the registered voice template by using acceptable information discrimination criterion. It is demonstrated that our approach may be used in existing biometric systems and technologies of speaker identification.

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