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    政大機構典藏 > 資訊學院 > 資訊科學系 > 學位論文 >  Item 140.119/148473
    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
    鄭詠儒
    Jeng, Yung-Ru
    Keywords: 音樂重組
    基因演算法
    深度學習
    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。
    基因演算法是常見的音樂自動作曲方法。現有透過基因演算法的音樂自動重
    組研究是以音符作為基本單位的來進行演化。但是以音符為單位的重組可能會破壞音樂的結構。此外,基因演算法往往必須設計適應函數,量化子代的品質以模擬演化的天則。隨著機器學習技術的發展,近年來已有運用學習式適應評估於電腦作曲的研究,以取代適應函數的設計。但針對音樂重組,尚未有學習式適應評估的研究。
    本研究旨在提出運用學習式適應評估且以樂句為單位的自動音樂重組。為確保樂曲風格的保留以及音樂的美感,本論文針對輸入的兩首歌曲,判斷樂曲結構並擷取出樂句並以樂句為單位進行基因演算法的演化來生成新的歌曲。此外,本論文運用深度學習中的長短期記憶模型,提出音樂重組的適應度評估模型以確保曲風的遺傳和美感的傳承。本論文邀請測試者評估生成樂曲,以驗證本論文所提出的方法之效果。
    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
    國立政治大學
    資訊科學系
    110753126
    Source URI: http://thesis.lib.nccu.edu.tw/record/#G0110753126
    Data Type: thesis
    Appears in Collections:[資訊科學系] 學位論文

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