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Conference Paper Acoustic Feature based Abnormal Diagnosis Techniques with Capsule Networks
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Authors
NacWoo Kim, HyunYong Lee, SangJun Park, JunGi Lee, ByungTak Lee
Issue Date
2020-10
Citation
International Conference on Information and Communication Technology Convergence (ICTC) 2020, pp.1147-1149
Publisher
IEEE
Language
English
Type
Conference Paper
DOI
https://dx.doi.org/10.1109/ICTC49870.2020.9289578
Abstract
This paper proposes a new method of converting acoustical data into mel-frequency cepstral coefficient feature vector and performing detection of abnormal acoustics using newly designed capsule network. Characteristic performance tests for extracting optimal acoustic characteristics through mel-frequency cepstral coefficient are first performed, and abnormal acoustic feature extraction model performance using capsule network is tested. Capsule network requires relatively fewer training sets compared to the convolutional neural network model, but it has equivariance properties for learning data and has strong characteristics for affine transformations. Our abnormal detection-based capsule network model shows superior performance compared to other abnormal detection models in terms of accuracy.
KSP Keywords
Acoustic characteristics, Acoustic feature extraction, Affine Transformation, Convolution neural network(CNN), Extraction model, Feature Vector, Learning data, Mel-Frequency Cepstrum Coefficients(MFCC), Mel-frequency cepstral, Model performance, Performance Test