인문학
사회과학
자연과학
공학
의약학
농수해양학
예술체육학
복합학
DBpia AI
AI 에이전트
AI 아이디어
AI 리더
AI 뷰어
Citeasy
크롬 서지관리 다운로드
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.
논문 기본 정보
- 저자정보
초록·키워드
To address the challenges of capturing long-term temporal dependencies, insufficient extraction of key emotional features, and the inherent limitations of single-modal information in music emotion recognition tasks, this study proposes a novel multi-modal framework. The core idea of the research is to break through the limitations of single mode in the expression of complex music emotions by separating and deeply mining the acoustic physical dynamics of audio and the music theory context of Musical Instrument Digital Interface (MIDI), and making adaptive fusion at the decision-making level. Specifically, the framework constructs a dual channel model based on long short-term memory (LSTM) networks and gated recurrent units (GRU). First, for symbolic music, a bidirectional LSTM with a self-attention mechanism is designed to capture music theory context and dynamically focus on crucial emotional phrases. Second, for audio music, a bidirectional GRU based on temporal pattern attention is constructed to perform deep temporal modeling of Mel spectrograms. Experimental results demonstrate that the symbolic model achieves an average accuracy of 88.68% on the MidiEmotion dataset, while the audio model attains 88.15% on the EMOPIA dataset. The multi-modal fusion model integrates the strengths of both, achieving a superior final accuracy of 91.55% and an F1-score of 91.04%. These findings prove that the proposed model effectively leverages the complementarity between symbolic semantics and acoustic features, substantially enhancing both the precision and robustness of emotion detection in music.
본문·목차
인공지능 문자 인식 모델을 통해 추출된 텍스트로, 일부 오타나 오류가 포함될 수 있으나 지속적으로 개선 중입니다.
오류를 발견하셨다면 해당 부분을 드래그한 후 ' 를 통해 신고해주세요.
오류를 발견하셨다면 해당 부분을 드래그한 후 ' 를 통해 신고해주세요.
최근 본 자료 전체보기
