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.
초록·키워드
Deep learning has made a lot of progress in the field of music generation. It now has powerful tools for both preserving traditional music and creating new, innovative compositions. This review explores various and recent deep learning models, such as Long Short-Term Memory (LSTM) networks, Transformer-based models, Reinforcement Learning (RL), and Diffusion-based architectures, and how they are applied to music generation. LSTMs effectively capture temporal dependencies, which are vital for producing coherent melodies and chord progressions. Transformer models, like MUSICGEN and STEMGEN, handle large amounts of data and dependencies efficiently, but they need a lot of computational resources. Reinforcement Learning models, such as MusicRL, combine human feedback to fine-tune AI-generated compositions based on the individual's preferences. Diffusion-based models, like MusicLDM, enhance audio fidelity, though real-time application remains a challenge. The objective of emotion-conditioned models, such as ECMusicLM, is to combine music with emotional cues so that the output has a stronger emotional resonance. However, each model faces its own set of limitations, such as computational inefficiency, data dependency, and challenges in capturing complex emotional nuances. Future research should focus on improving the computational efficiency of these models, expanding training datasets, and integrating more interactive, real-time systems.
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.
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