Recognition of isolated digits using DNN–HMM and harmonic noise model

Guerid, Abdelkader; Houacine, Amrane

doi:10.1049/iet-spr.2018.5131

Cited by 7 publications

(2 citation statements)

References 24 publications

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“…Clustering methods have acquired many successful applications in the past decades [1][2][3][4], such as K-Means [5] and spectral clustering [6]. Recently, deep clustering methods attract more attention [7][8][9][10][11][12], which transfers the original samples into a deep network to learn an abstract and discriminative feature representation. Different from the above traditional methods, deep clustering methods learn the in-depth information.…”

Section: Introductionmentioning

confidence: 99%

Multi‐graph convolutional clustering network

Wang

et al. 2022

IET Signal Processing

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The relationship between objects can be described from different angles. Although multiple kinds of relationships make the connections between objects complex, they bring in more discriminative information for the clustering tasks. Therefore, how to effectively fuse multiple kinds of relationships becomes a critical problem. In this paper, we propose a novel Multi-graph Convolutional Clustering Network which deeply explores the feature information of nodes and fuses the multiple kinds of relationships between nodes. Unlike most graph convolutional clustering methods that only exploit the single graph or directly fuse multiple graphs into a unified graph before the graph convolution operation, we firstly build multiple parallelled graph convolution layers for each graph to learn diverse data representations, which fully exploits different statistics information between graphs. Then, a designed multi-graph attention module fuses above data representations and considers the importance of each graph. Besides, the proposed model completes the transition from single graph to multiple graphs, which reduces the dependence of the quality of the single graph and enhances the robustness to graphs. Experimental results verify that the proposed multi-graph convolution clustering performs better than the traditional single-graph convolution clustering.

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Section: Introductionmentioning

confidence: 99%

Multi‐graph convolutional clustering network

Wang

et al. 2022

IET Signal Processing

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“…However, the MFCC method is limited to the frequency conversion relationship [16]. In order to make full use of MFCC method to extract vibration signal features, we need to improve its Mel frequency scale [17]. Pliwak proposed an adaptive frequency scale (SFCC) based on human voice to improve the MFCC feature extraction method [18], and achieved good results compared with the traditional MFCC feature extraction results.…”

Section: Introductionmentioning

confidence: 99%

Feature Classification Method of Frequency Cepstrum Coefficient Based On Weighted Extreme Gradient Boosting

Wang

Zhang

et al. 2021

IEEE Access

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Cepstrum coefficient and decision tree classification provide a feasible method for mechanical fault diagnosis. However, this method has the problems of low accuracy of feature extraction and low reliability of feature classification. The efficiency and classification results of this method are not ideal. Therefore, this paper proposes a feature classification method of frequency cepstrum coefficients based on weighted extreme gradient boosting. The proposed method is mainly divided into two parts: feature extraction and feature classification. In the part of feature extraction, Considering the limitation of Mel frequency scale in MFCC, an adaptive frequency cepstrum coefficient (AFCC) method is proposed, which can adaptively obtain the frequency scale of the corresponding signal, thus improving the feature accuracy extracted by MFCC method. In the aspect of feature classification, XGboost algorithm has strong learning ability, which easily leads to over fitting and affects the classification accuracy, A weighted extreme gradient boosting (WXGB) method is proposed, and an improved distance evaluation method (IDE) is introduced to modify the feature weights learned by XGboost, so as to improve the prediction accuracy of the classification model. In order to verify the effectiveness of the method, the vibration signals of rolling bearings with different fault types are taken as samples to carry out feature extraction and classification. The results of feature extraction and classification of different methods are compared, and the effectiveness of the proposed feature classification method based on weighted extremum gradient boosting frequency cepstrum coefficient in rolling bearing fault diagnosis is verified.

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Research on Adaptive Adjustment Algorithm of Isolated Word State Number Based on ANN-HMM Mixed Model

Yin

Shen

2020

Communications in Computer and Information Science

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Recognition of isolated digits using DNN–HMM and harmonic noise model

Cited by 7 publications

References 24 publications

Multi‐graph convolutional clustering network

Multi‐graph convolutional clustering network

Feature Classification Method of Frequency Cepstrum Coefficient Based On Weighted Extreme Gradient Boosting

Research on Adaptive Adjustment Algorithm of Isolated Word State Number Based on ANN-HMM Mixed Model

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