인문학
사회과학
자연과학
공학
의약학
농수해양학
예술체육학
복합학
DBpia AI
AI 에이전트
AI 아이디어
AI 리더
AI 뷰어
Citeasy
크롬 서지관리 다운로드
개인구독
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지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
초록·키워드
Categorizing music pieces by composer is a challenging task in digital music processing due to their highly flexible structures, introducing subjective interpretation by individuals. This research utilized musical data from the MIDI and audio edited for synchronous tracks and organization dataset of virtuosic piano pieces. In this work, pitch and duration were the musical features of interest. The goal was to innovate an approach to representing a musical piece using SentencePiece and Word2vec, which are natural language processing-based techniques. We attempted to find correlated melodies that are likely to be formed by single interpretable units of music via co-occurring notes, and represented them as a musical word/subword vector. Composer classification was performed in order to ensure the efficiency of this musical data representation scheme. Among classification machine learning algorithms, k-nearest neighbors, random forest classifier, logistic regression, support vector machines, and multilayer perceptron were employed to compare performances. In the experiment, the feature extraction methods, classification algorithms, and music window sizes were varied. The results were that classification performance was sensitive to feature extraction methods. Musical word/subword vector standard deviation was the most effective feature, resulting in classification with a high F1-score, attaining 1.00. No significant difference was observed among model classification performances.
#Computer science
#Artificial intelligence
#Melody
#Support vector machine
#Speech recognition
#Music information retrieval
#MIDI
#Random forest
#Classifier (UML)
#Feature extraction
#Natural language processing
#Feature vector
#Perceptron
#Pattern recognition (psychology)
#Machine learning
#Musical
#Artificial neural network
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