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    Please use this identifier to cite or link to this item: https://nccur.lib.nccu.edu.tw/handle/140.119/148473

    Title: 運用學習式適應評估以樂句為單位的自動音樂重組
    Phrase-based Automatic Music Recombination Using Learning-based Fitness Evaluation
    Authors: 鄭詠儒
    Jeng, Yung-Ru
    Contributors: 沈錳坤
    Shan, Man-Kwan
    Jeng, Yung-Ru
    Keywords: 音樂重組
    Music Recombination
    Genetic Algorithms
    Deep Learning
    Date: 2023
    Issue Date: 2023-12-01 10:33:26 (UTC+8)
    Abstract: 音樂自動作曲在電腦音樂領域發展行之有年,音樂自動作曲常見的作曲方法
    如 Markov Chain、 Evolutionary Algorithm、 Neural Network等。音樂重組是電腦音樂自動作曲的一種方式。輸入兩首歌曲,音樂重組會輸出一首重新組合的歌曲,且希望保留兩首輸入歌曲的風格。
    Automatic music composition has a long-standing history in the field of computer music, with common composition methods such as Markov Chains, Evolutionary Algorithms, Neural Networks. Music recombination is one approach within computer music composition where two songs are input, and music recombination generates a newly composed song while aiming to preserve the styles of the two input songs.
    Genetic algorithm has been a prominent method for automatic music composition. Existing research on music recombination using genetic algorithms typically evolves music based on individual musical notes. However, recombining at the note level may disrupt the musical structure. Moreover, with the development of machine learning techniques, recent studies in music composition have explored the use of learning-based fitness evaluation to replace the manual design of fitness functions. However, no research has been done on the learning-based fitness evaluation for music recombination.
    This thesis aims to propose a phrase-based music recombination approach that utilizes learning-based fitness evaluation. To ensure the preservation of musical styles and aesthetic qualities, this thesis analyzes the structure of music, extracts musical phrases, and employs a genetic algorithm that evolves new songs at the phrase level. Furthermore, this research proposes the fitness evaluation model for music recombination by utilizing Long Short-Term Memory (LSTM) models to ensuring the inheritance of musical style and aesthetics. The experiments evaluated by subjects are performed to show the effectiveness of the proposed approach.
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    Description: 碩士
    Source URI: http://thesis.lib.nccu.edu.tw/record/#G0110753126
    Data Type: thesis
    Appears in Collections:[Department of Computer Science ] Theses

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