상세정보
학술대회
Acoustic Feature based Abnormal Diagnosis Techniques with Capsule Networks
- 발행일
- 202010
- 출처
- International Conference on Information and Communication Technology Convergence (ICTC) 2020, pp.1147-1149
- 협약과제
- 20ZK1100, 호남권 지역산업 기반 ICT 융합기술 고도화 지원사업, 이길행
- 초록
- 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 제안 키워드
- 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