scholarly journals A Wavelet Packet and Mel-Frequency Cepstral Coefficients-Based Feature Extraction Method for Speaker Identification

2015 ◽  
Vol 61 ◽  
pp. 416-421 ◽  
Author(s):  
Claude Turner ◽  
Anthony Joseph
Keyword(s):  
Feature Extraction ◽  
Extraction Method ◽  
Speaker Identification ◽  
Wavelet Packet ◽  
Mel Frequency Cepstral Coefficients ◽  
Feature Extraction Method ◽  
Cepstral Coefficients

Hot Strip Tail Flick Recognition Based on Morphological Pattern Spectrum

2012 ◽  
Vol 572 ◽  
pp. 25-30
Author(s):  
Li Jing Han ◽  
Jian Hong Yang ◽  
Min Lin ◽  
Jin Wu Xu
Keyword(s):  
Feature Extraction ◽  
Extraction Method ◽  
Steel Strip ◽  
Wavelet Packet ◽  
Support Vector ◽  
Tail Flick ◽  
Morphological Pattern ◽  
Feature Extraction Method ◽  
Pattern Spectrum ◽  
Hot Strip

Hot strip tail flick is an abnormal production phenomenon, which brings many damages. To recognize the tail flick signals from all throwing steel strip signals, a feature extraction method based on morphological pattern spectrum is proposed in this paper. The area between signal curves after multiscale opening operation and the horizontal axis is computed as the pattern spectrum value and it reflects the geometric information differences. Then, support vector machine is used as the classifier. Experimental results show that the total correct rate based on pattern spectrum feature reached 96.5%. Compared with wavelet packet energy feature, the total correct rate is 92.1%. So, the feasibility and availability of this new feature extraction method are verified.

scholarly journals Comparison of feature extraction and normalization methods for speaker recognition using grid-audiovisual database

2020 ◽  
Vol 18 (2) ◽  
pp. 782
Author(s):  
Musab T. S. Al-Kaltakchi ◽  
Haithem Abd Al-Raheem Taha ◽  
Mohanad Abd Shehab ◽  
Mohamed A.M. Abdullah
Keyword(s):  
Feature Extraction ◽  
Speaker Recognition ◽  
Speaker Identification ◽  
Gaussian Mixture ◽  
Identification Accuracy ◽  
Identification System ◽  
Good Representation ◽  
Mel Frequency Cepstral Coefficients ◽  
Normalization Methods ◽  
Cepstral Coefficients

<p><span lang="EN-GB">In this paper, different feature extraction and feature normalization methods are investigated for speaker recognition. With a view to give a good representation of acoustic speech signals, Power Normalized Cepstral Coefficients (PNCCs) and Mel Frequency Cepstral Coefficients (MFCCs) are employed for feature extraction. Then, to mitigate the effect of linear channel, Cepstral Mean-Variance Normalization (CMVN) and feature warping are utilized. The current paper investigates Text-independent speaker identification system by using 16 coefficients from both the MFCCs and PNCCs features. Eight different speakers are selected from the GRID-Audiovisual database with two females and six males. The speakers are modeled using the coupling between the Universal Background Model and Gaussian Mixture Models (GMM-UBM) in order to get a fast scoring technique and better performance. The system shows 100% in terms of speaker identification accuracy. The results illustrated that PNCCs features have better performance compared to the MFCCs features to identify females compared to male speakers. Furthermore, feature wrapping reported better performance compared to the CMVN method. </span></p>

scholarly journals Online monitoring and diagnosis of high voltage circuit breaker faults: feature extraction analysis of vibration signals

2019 ◽  
Vol 10 ◽  
pp. 13 ◽  
Author(s):  
Long Li ◽  
Jianfeng Xiao ◽  
Bin Wu ◽  
Mengge Zhou ◽  
Qian Wang
Keyword(s):  
Feature Extraction ◽  
High Voltage ◽  
Extraction Method ◽  
Wavelet Packet ◽  
Circuit Breaker ◽  
Vibration Signal ◽  
Normal Operation ◽  
Support Vector ◽  
Feature Extraction Method ◽  
High Voltage Circuit Breaker

The development of power grid system not only increases voltage and capacity, but also increases power risk. This paper briefly introduces the feature extraction method of the vibration signal of high voltage circuit breaker and support vector machine (SVM) algorithm and then analyzed the high voltage circuit breaker in three states: normal operation, fixed screw loosening and falling of opening spring, using the SVM based on the above feature extraction method. The results showed that the accuracy and precision rates of fault identification of circuit breaker were the highest by using the wavelet packet energy entropy extraction features, the false alarm rate was the lowest, and the detection time was the shortest.

Border and Coastal Surveillance Sound Target Classification Based on Optimum Wavelet Packet Decomposition

2011 ◽  
Vol 225-226 ◽  
pp. 725-728 ◽  
Author(s):  
Yan Wang ◽  
Zhi Li
Keyword(s):  
Feature Extraction ◽  
Bp Neural Network ◽  
Extraction Method ◽  
Wavelet Packet ◽  
Wavelet Packet Decomposition ◽  
Target Classification ◽  
Feature Extraction Method ◽  
Multi Scale ◽  
New Feature ◽  
Sound Target

The present work contributes to the field of border and coastal surveillance sound target classification. A new feature extraction method is proposed based on the optimum wavelet packet decomposition (OWPD). According to the frequency characteristic of border and coastal surveillance sound signals, each signal is decomposed by selective multi-scale wavelet packet decomposition (WPD) and the OWPD tree is obtained. From their high dimension OWPD coefficients, we build the meaningful and compact energy feature vectors, then use them as the input vectors of the BP neural network to classify the border and coastal surveillance sound types. Extensive experimental results show that the classification efficiency is up to 94% using this feature extraction method, improved 6% compared with the method based on WPD.

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