KIPS Transactions on Software and Data Engineering (정보처리학회논문지:소프트웨어 및 데이터공학)

Korea Information Processing Society (한국정보처리학회)
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Performance Comparison of Machine Learning Algorithms for TAB Digit Recognition

타브 숫자 인식을 위한 기계 학습 알고리즘의 성능 비교

  • 허재혁 (단국대학교 컴퓨터공학과) ;
  • 이현종 (단국대학교 소프트웨어학과) ;
  • 황두성 (단국대학교 소프트웨어학과)
  • Received : 2018.06.05
  • Accepted : 2018.07.25
  • Published : 2019.01.31
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Abstract

In this paper, the classification performance of learning algorithms is compared for TAB digit recognition. The TAB digits that are segmented from TAB musical notes contain TAB lines and musical symbols. The labeling method and non-linear filter are designed and applied to extract fret digits only. The shift operation of the 4 directions is applied to generate more data. The selected models are Bayesian classifier, support vector machine, prototype based learning, multi-layer perceptron, and convolutional neural network. The result shows that the mean accuracy of the Bayesian classifier is about 85.0% while that of the others reaches more than 99.0%. In addition, the convolutional neural network outperforms the others in terms of generalization and the step of the data preprocessing.

본 논문에서는 기타 타브 악보에서 추출한 프렛 번호를 대상으로 학습 알고리즘의 분류 성능을 비교한다. 타브 악보로부터 세그먼트를 통해 추출된 타브 숫자 데이터는 타브 선과 악보 기호가 포함하기 때문에 레이블링 기법과 비선형 필터를 이용하여 프렛 숫자를 추출한다. 추가적인 데이터 확보를 위해 전처리가 수행된 데이터에 대해 4 방향으로 이동 연산을 수행한다. 선택된 학습 모델은 베이지안 분류기, 지지벡터기기, 프로토타입 기반 학습, 다층 신경망 그리고 합성곱 신경망 모델 등이다. 실험 결과 베이지안 분류기는 85.0% 평균 정확도를 보였고 나머지 분류기는 99.0% 이상의 평균 정확도를 보였다. 일반화 성능과 전처리 단계를 고려 시 합성곱 신경망이 다른 학습 모델들보다 우수하다.

Keywords

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Fig. 1. The Process for Extracting Fret Digits

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Fig. 2. Examples of TAB Digit Segmentation

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Fig. 3. Removing TAB Lines

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Fig. 4. The Limitation of Labeling Method

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Fig. 5. The Example of Non-linear Filtering

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Fig. 6. Examples of Segmented Data andPreprocessed Data

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Fig. 7. A Visualization of TAB Digits Using the t-SNE Method

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Fig. 8. Feature Map Examples

Table 1. The Number of Data Per Segment Size

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Table 2. The Number of Fret Digits Per Class

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Table 3. The Performance Comparison of TAB Digit Recognition

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Table 4. The Structure of a MLP Network

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Table 5. The Structure of a CNN-PRE Network

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Table 6. The Structure of a CNN-ORG Network

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