A Selection Method of Vibration Signal Feature Based on Mutual Information and Distance Measurement

2012 ◽  
Vol 1 (1) ◽  
pp. a44
Keyword(s):  
Mutual Information ◽  
Vibration Signal ◽  
Selection Method ◽  
Distance Measurement ◽  
Feature Based

Feature Selection Method Based on Mutual Information and Support Vector Machine

2021 ◽  
pp. 2150021
Author(s):  
Gang Liu ◽  
Chunlei Yang ◽  
Sen Liu ◽  
Chunbao Xiao ◽  
Bin Song
Keyword(s):  
Support Vector Machine ◽  
Feature Selection ◽  
Mutual Information ◽  
Classification Accuracy ◽  
Feature Selection Method ◽  
Selection Method ◽  
Support Vector ◽  
Svm Classifier ◽  
Standard Data ◽  
Feature Dimension

A feature selection method based on mutual information and support vector machine (SVM) is proposed in order to eliminate redundant feature and improve classification accuracy. First, local correlation between features and overall correlation is calculated by mutual information. The correlation reflects the information inclusion relationship between features, so the features are evaluated and redundant features are eliminated with analyzing the correlation. Subsequently, the concept of mean impact value (MIV) is defined and the influence degree of input variables on output variables for SVM network based on MIV is calculated. The importance weights of the features described with MIV are sorted by descending order. Finally, the SVM classifier is used to implement feature selection according to the classification accuracy of feature combination which takes MIV order of feature as a reference. The simulation experiments are carried out with three standard data sets of UCI, and the results show that this method can not only effectively reduce the feature dimension and high classification accuracy, but also ensure good robustness.

scholarly journals Prosodic Feature-Based Discriminatively Trained Low Resource Speech Recognition System

2022 ◽  
Vol 14 (2) ◽  
pp. 614
Author(s):  
Taniya Hasija ◽  
Virender Kadyan ◽  
Kalpna Guleria ◽  
Abdullah Alharbi ◽  
Hashem Alyami ◽  
...  
Keyword(s):  
Speech Recognition ◽  
Mutual Information ◽  
Data Augmentation ◽  
Recognition System ◽  
Speech Recognition System ◽  
Prosodic Features ◽  
Prosodic Feature ◽  
Feature Based ◽  
Maximum Mutual Information ◽  
Children's Speech

Speech recognition has been an active field of research in the last few decades since it facilitates better human–computer interaction. Native language automatic speech recognition (ASR) systems are still underdeveloped. Punjabi ASR systems are in their infancy stage because most research has been conducted only on adult speech systems; however, less work has been performed on Punjabi children’s ASR systems. This research aimed to build a prosodic feature-based automatic children speech recognition system using discriminative modeling techniques. The corpus of Punjabi children’s speech has various runtime challenges, such as acoustic variations with varying speakers’ ages. Efforts were made to implement out-domain data augmentation to overcome such issues using Tacotron-based text to a speech synthesizer. The prosodic features were extracted from Punjabi children’s speech corpus, then particular prosodic features were coupled with Mel Frequency Cepstral Coefficient (MFCC) features before being submitted to an ASR framework. The system modeling process investigated various approaches, which included Maximum Mutual Information (MMI), Boosted Maximum Mutual Information (bMMI), and feature-based Maximum Mutual Information (fMMI). The out-domain data augmentation was performed to enhance the corpus. After that, prosodic features were also extracted from the extended corpus, and experiments were conducted on both individual and integrated prosodic-based acoustic features. It was observed that the fMMI technique exhibited 20% to 25% relative improvement in word error rate compared with MMI and bMMI techniques. Further, it was enhanced using an augmented dataset and hybrid front-end features (MFCC + POV + Fo + Voice quality) with a relative improvement of 13% compared with the earlier baseline system.

scholarly journals Mini-Batch Normalized Mutual Information: A Hybrid Feature Selection Method

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 116875-116885 ◽  
Author(s):  
G. S. Thejas ◽  
Sajal Raj Joshi ◽  
S. S. Iyengar ◽  
N. R. Sunitha ◽  
Prajwal Badrinath
Keyword(s):  
Feature Selection ◽  
Mutual Information ◽  
Feature Selection Method ◽  
Selection Method ◽  
Normalized Mutual Information

Feature Analysis and Extraction of Complex Motion Target Based on Coherent Laser Remote Sensing Detection

2012 ◽  
Vol 588-589 ◽  
pp. 1076-1080
Author(s):  
Zheng Li ◽  
Yi Hua Hu ◽  
Fei Yan
Keyword(s):  
Remote Sensing ◽  
Vibration Signal ◽  
Complex Motion ◽  
Hilbert Huang Transform ◽  
High Measurement ◽  
Inherent Frequency ◽  
Feature Based ◽  
Civil Values ◽  
Destructive Measurement ◽  
Laser Remote Sensing

Vibration features of moving targets can reflect their detailed characteristics, which have important military and civil values. Coherent lidar is the preponderant method of target detection, because of its advantages such as high measurement accuracy and ability of long-distant non-destructive measurement, which is appropriate for detecting the vibration information of the target through remote sensing. Traditional analysis of moving target’s vibration always contained only one motion mode, which could not reflect the real complex motion of the target. This paper proposed a novel model of complex moving target’s vibration for coherent laser remote sensing detection. Considering the vibration signal is non-linear and non-stationary, Hilbert-Huang transform (HHT) was applied to the decomposition of the signal. After decomposition, the energy of the vibrating signal in some inherent frequency band was selected as the feature of the signal. Simulations and experiments were carried out to verify the divisibility of the feature, which could support the identification of target vibration feature based on coherent laser remote sensing detection.

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