Recognition of Acoustic Signals of Synchronous Motors with the Use of MoFS and Selected Classifiers

Głowacz, Adam

doi:10.1515/msr-2015-0024

Cited by 41 publications

(24 citation statements)

References 32 publications

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“…The proposed methods have many important applications: -It can be used for template creation for pattern recognition purposes, especially for distance-based methods and clustering [18,19,22,24,35,52]. Classification can be done for example with DTW with features set designed in similar way as we presented in Section 3.5 of this paper.…”

Section: Resultsmentioning

confidence: 99%

Averaging of motion capture recordings for movements’ templates generation

Hachaj

Koptyra

Ogiela

2018

Multimed Tools Appl

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In this paper we propose, describe and evaluate the novel motion capture (MoCap) data averaging framework. It incorporates hierarchical kinematic model, angle coordinates' preprocessing methods, that recalculate the original MoCap recording making it applicable for further averaging algorithms, and finally signals averaging processing. We have tested two signal averaging methods namely Kalman Filter (KF) and Dynamic Time Warping barycenter averaging (DBA). The propose methods have been tested on MoCap recordings of elite Karate athlete, multiple champion of Oyama karate knockdown kumite who performed 28 different karate techniques repeated 10 times each. The proposed methods proved to have not only high effectiveness measured with root-mean-square deviation (4.04 ± 5.03 degrees for KF and 5.57 ± 6.27 for DBA) and normalized Dynamic Time Warping distance (0.90 ± 1.58 degrees for KF and 0.93 ± 1.23 for DBA), but also the reconstruction and visualization of those recordings persists all crucial aspects of those complicated actions. The proposed methodology has many important applications in classification, clustering, kinematic analysis and coaching. Our approach generates an averaged full body motion template that can be practically used for example for human actions Multimed Tools Appl (2018) 77:30353-30380 recognition. In order to prove it we have evaluated templates generated by our method in human action classification tasks using DTW classifier. We have made two experiments. In first leave -one -out cross -validation we have obtained 100% correct recognitions. In second experiment when we classified recordings of one person using templates of another recognition rate 94.2% was obtained.

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Section: Resultsmentioning

confidence: 99%

Averaging of motion capture recordings for movements’ templates generation

Hachaj

Koptyra

Ogiela

2018

Multimed Tools Appl

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“…A typical echo state neural network has three layers such as an input layer, middle layer and an output layer. The activation function [25,21] of the input layer, middle layer and the output layers at time t can be calculated by the following equation. W S(t) = (s 1 (t) + s 2 (t) + ..... s n (t))…”

Section: Esnn With Texture Topologymentioning

confidence: 99%

Texture Based Automated Segmentation of Skin Lesions using Echo State Neural Networks

Khan¹,

Ganapathi²

2017

Journal of Electrical Engineering and Technology

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-A novel method of Skin lesion segmentation based on the combination of Texture and Neural Network is proposed in this paper. This paper combines the textures of different pixels in the skin images in order to increase the performance of lesion segmentation. For segmenting skin lesions, a two-step process is done. First, automatic border detection is performed to separate the lesion from the background skin. This begins by identifying the features that represent the lesion border clearly by the process of Texture analysis. In the second step, the obtained features are given as input towards the Recurrent Echo state neural networks in order to obtain the segmented skin lesion region. The proposed algorithm is trained and tested for 862 skin lesion images in order to evaluate the accuracy of segmentation. Overall accuracy of the proposed method is compared with existing algorithms. An average accuracy of 98.8% for segmenting skin lesion images has been obtained.

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“…In the area of the GMM-based speaker [28]- [30], as well as the acoustic signal recognition [31], the most commonly used spectral features are mel-frequency cepstral coefficients together with energy and prosodic parameters. In our experiments the features differ for ANOVA-based evaluation and GMM-based classification of the speech signal quality.…”

Section: Determination Of Features Of the Speech Signalmentioning

confidence: 99%

Two Methods of Automatic Evaluation of Speech Signal Enhancement Recorded in the Open-Air MRI Environment

Přibil

Přibilová²,

Frollo

2017

Measurement Science Review

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The paper focuses on two methods of evaluation of successfulness of speech signal enhancement recorded in the open-air magnetic resonance imager during phonation for the 3D human vocal tract modeling. The first approach enables to obtain a comparison based on statistical analysis by ANOVA and hypothesis tests. The second method is based on classification by Gaussian mixture models (GMM). The performed experiments have confirmed that the proposed ANOVA and GMM classifiers for automatic evaluation of the speech quality are functional and produce fully comparable results with the standard evaluation based on the listening test method.

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Recognition of Acoustic Signals of Synchronous Motors with the Use of MoFS and Selected Classifiers

Cited by 41 publications

References 32 publications

Averaging of motion capture recordings for movements’ templates generation

Averaging of motion capture recordings for movements’ templates generation

Texture Based Automated Segmentation of Skin Lesions using Echo State Neural Networks

Two Methods of Automatic Evaluation of Speech Signal Enhancement Recorded in the Open-Air MRI Environment

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