Statistical feature extraction method for wood species recognition system

2017 ◽  
Vol 76 (1) ◽  
pp. 345-356 ◽  
Author(s):  
Imanurfatiehah Ibrahim ◽  
Anis Salwa Mohd Khairuddin ◽  
Hamzah Arof ◽  
Rubiyah Yusof ◽  
Effariza Hanafi
Keyword(s):  
Feature Extraction ◽  
Wood Species ◽  
Extraction Method ◽  
Species Recognition ◽  
Recognition System ◽  
Feature Extraction Method ◽  
Statistical Feature

A novel statistical feature extraction method for textual images: Optical font recognition

2012 ◽  
Vol 39 (5) ◽  
pp. 5470-5477 ◽  
Author(s):  
Bilal Bataineh ◽  
Siti Norul Huda Sheikh Abdullah ◽  
Khairuddin Omar
Keyword(s):  
Feature Extraction ◽  
Extraction Method ◽  
Feature Extraction Method ◽  
Statistical Feature ◽  
Font Recognition

scholarly journals A Multiscale Chaotic Feature Extraction Method for Speaker Recognition

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-9
Author(s):  
Jiang Lin ◽  
Yi Yumei ◽  
Zhang Maosheng ◽  
Chen Defeng ◽  
Wang Chao ◽  
...  
Keyword(s):  
Feature Extraction ◽  
Speaker Recognition ◽  
Extraction Method ◽  
State Of The Art ◽  
Recognition System ◽  
Nonlinear Dynamic Model ◽  
Feature Extraction Method ◽  
Analysis Technique ◽  
Recognition Systems ◽  
Environment Noise

In speaker recognition systems, feature extraction is a challenging task under environment noise conditions. To improve the robustness of the feature, we proposed a multiscale chaotic feature for speaker recognition. We use a multiresolution analysis technique to capture more finer information on different speakers in the frequency domain. Then, we extracted the speech chaotic characteristics based on the nonlinear dynamic model, which helps to improve the discrimination of features. Finally, we use a GMM-UBM model to develop a speaker recognition system. Our experimental results verified its good performance. Under clean speech and noise speech conditions, the ERR value of our method is reduced by 13.94% and 26.5% compared with the state-of-the-art method, respectively.

A robust feature extraction method based on monogenic filter for iris recognition system

2014 ◽  
Author(s):  
Walid Aydi ◽  
Nade Fadhel ◽  
Nouri Masmoudi ◽  
Lotfi Kamoun
Keyword(s):  
Feature Extraction ◽  
Extraction Method ◽  
Iris Recognition ◽  
Recognition System ◽  
Feature Extraction Method ◽  
Robust Feature Extraction

A new proposed statistical feature extraction method in speech emotion recognition

2021 ◽  
Vol 93 ◽  
pp. 107172
Author(s):  
Husam Ali Abdulmohsin ◽  
Hala Bahjat Abdul wahab ◽  
Abdul Mohssen Jaber Abdul hossen
Keyword(s):  
Feature Extraction ◽  
Emotion Recognition ◽  
Extraction Method ◽  
Speech Emotion Recognition ◽  
Feature Extraction Method ◽  
Statistical Feature

Skeleton-Based Feature Extraction Method for Two-Dimensional Potential Energy

2010 ◽  
Vol 139-141 ◽  
pp. 2051-2054 ◽  
Author(s):  
Xue Song Chen ◽  
Cheng Wang ◽  
Xue Jun Xu ◽  
Hong Bo Zhu ◽  
Shao Hua Jiang
Keyword(s):  
Feature Extraction ◽  
Potential Energy ◽  
Extraction Method ◽  
Target Recognition ◽  
Recognition System ◽  
Image Feature ◽  
Feature Extraction Method ◽  
Structure Information ◽  
The Matrix ◽  
Point Projection

A good feature extraction method can improve the performance of pattern recognition system or classification system. Using potential energy theory into binary image feature extraction and feature store is a new method for image processing. The skeleton can be better display the whole features of the object. In target recognition system, using potential energy of skeleton-point projection into the plane coordinate system. The method can be better to show a skeleton in the structural feature. In addition, it can better avoid the matrix storage redundancy. In all energy projection method, potential energy projection is better shown its superiority in the structure information, the time of consumption and the storage space. The skeleton potential energy can be used in target recognition and target classification field and so on.

scholarly journals A Robust Feature Extraction Method for Real-Time Speech Recognition System on a Raspberry Pi 3 Board

2019 ◽  
Vol 9 (2) ◽  
pp. 4066-4070 ◽  
Author(s):  
A. Mnassri ◽  
M. Bennasr ◽  
C. Adnane
Keyword(s):  
Feature Extraction ◽  
Speech Recognition ◽  
Real Time ◽  
Extraction Method ◽  
Recognition System ◽  
Raspberry Pi ◽  
Discrete Wavelet ◽  
Speech Recognition System ◽  
Feature Extraction Method ◽  
Robust Feature Extraction

The development of a real-time automatic speech recognition system (ASR) better adapted to environmental variabilities, such as noisy surroundings, speaker variations and accents has become a high priority. Robustness is required, and it can be performed at the feature extraction stage which avoids the need for other pre-processing steps. In this paper, a new robust feature extraction method for real-time ASR system is presented. A combination of Mel-frequency cepstral coefficients (MFCC) and discrete wavelet transform (DWT) is proposed. This hybrid system can conserve more extracted speech features which tend to be invariant to noise. The main idea is to extract MFCC features by denoising the obtained coefficients in the wavelet domain by using a median filter (MF). The proposed system has been implemented on Raspberry Pi 3 which is a suitable platform for real-time requirements. The experiments showed a high recognition rate (100%) in clean environment and satisfying results (ranging from 80% to 100%) in noisy environments at different signal to noise ratios (SNRs).

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