發(fā)布時間：2018-05-07 17:00

  本文選題：算法作曲 + 遺傳算法　； 參考：《河南師范大學(xué)》2014年碩士論文

【摘要】：隨著計算機技術(shù)的不斷發(fā)展，自動化技術(shù)已被廣泛應(yīng)用于旋律創(chuàng)作領(lǐng)域。算法作曲的出現(xiàn)，使得旋律創(chuàng)作變得容易、方便。作為算法作曲的方法之一，遺傳算法能夠靈活地產(chǎn)生各種不同風(fēng)格的旋律。但是，應(yīng)用遺傳算法進行旋律創(chuàng)作時，適應(yīng)度函數(shù)的構(gòu)建仍是一大挑戰(zhàn)。以往的適應(yīng)度函數(shù)大多數(shù)很主觀而且容易受設(shè)計者的偏見影響，評估過程耗費時間長且效率低下。本文著重研究將遺傳算法應(yīng)用于旋律創(chuàng)作時適應(yīng)度函數(shù)的設(shè)計及其他相關(guān)問題。 首先，分析了將遺傳算法應(yīng)用于旋律創(chuàng)作時生成初始群體常用的幾種人工智能方法，包括數(shù)學(xué)模型的方法、人工神經(jīng)網(wǎng)絡(luò)的方法、音樂文法的方法等。為了實時產(chǎn)生旋律，本文中采用基于數(shù)學(xué)模型的方法來生成初始群體。并且為了避免生成的群體過度隨機，在生成初始群體時對相關(guān)旋律參數(shù)進行了限制，如調(diào)號、拍號等。其次，設(shè)計了一種新的旋律編碼方式，即以旋律中的每個音符為單位，基于該音符的音級、時值和符號對旋律進行編碼。該編碼方法可以直觀形象地表現(xiàn)旋律的信息，而且能夠保證旋律信息的正確性。再次，，構(gòu)建了旋律特征提取器，定義了用于自動評價的多目標(biāo)適應(yīng)度函數(shù)。特征提取器用于提取旋律樂譜的特征，如旋律中各音符的自相似性，旋律的圖形，音符的線性度，所使用的音域，等等。將這些特征進行線性組合作為多目標(biāo)適應(yīng)度函數(shù)，對所有生成的旋律個體進行適應(yīng)度計算，根據(jù)計算得到的適應(yīng)度函數(shù)值決定個體是否需要進化。然后，對進化過程中的交叉和變異操作作了研究，以使旋律的進化能夠順利進行。交叉操作時著重考慮了音符的時值修正問題，確保交叉前后每一小節(jié)中音符的時值之和保持不變。執(zhí)行變異操作時，對于音級的變異，保證相鄰音符的音程不超過8度。最后，搭建了基于多目標(biāo)適應(yīng)度函數(shù)的遺傳算法旋律創(chuàng)作實驗系統(tǒng)平臺。實驗結(jié)果表明，該系統(tǒng)在確保生成具有較高質(zhì)量的旋律的前提下，縮短了評估所用的時間，大大提高了旋律創(chuàng)作的效率。
[Abstract]:With the development of computer technology, automation technology has been widely used in melody creation field. The emergence of algorithm composition makes melody creation easy and convenient. As one of the algorithms to compose music, genetic algorithm can produce various melodies flexibly. However, the construction of fitness function is still a challenge when using genetic algorithm to create melody. Most of the previous fitness functions are subjective and susceptible to designer bias, and the evaluation process is time-consuming and inefficient. This paper focuses on the application of genetic algorithm to the design of fitness function in melody creation and other related problems. Firstly, several artificial intelligence methods which are commonly used to generate initial population when genetic algorithm is applied to melody creation are analyzed, including mathematical model method, artificial neural network method, music grammar method and so on. In order to generate melody in real time, the method based on mathematical model is used to generate initial group. In order to avoid the excessive random of the generated group, the related melody parameters, such as tone, racquet, etc, are restricted when the initial group is generated. Secondly, a new melodic coding method is designed, in which each note in the melody is taken as the unit, and the melody is encoded based on the tone level, time value and symbol of the note. This coding method can express melody information intuitively and guarantee the correctness of melody information. Thirdly, a melody feature extractor is constructed and a multi-objective fitness function for automatic evaluation is defined. The feature extractor is used to extract the features of melodic music, such as the self-similarity of the notes in the melody, the graph of the melody, the linearity of the notes, the range used, and so on. The linear combination of these features is used as a multi-objective fitness function to calculate the fitness of all the generated melody individuals and determine whether the individual needs to evolve according to the calculated fitness function. Then, the crossover and mutation operations in the evolution process are studied to make the melodic evolution proceed smoothly. In order to ensure that the sum of the time values of the notes in each section before and after the crossover is kept unchanged, the time value correction problem of the notes is considered in the crossover operation. When performing mutation operation, ensure that the interval of adjacent notes is not more than 8 degrees for the variation of tone level. Finally, a multi-objective fitness function based on genetic algorithm melody creation experimental system platform is built. The experimental results show that the system can shorten the time of evaluation and greatly improve the efficiency of melody creation on the premise of creating melody with high quality.
【學(xué)位授予單位】：河南師范大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2014
【分類號】：TP18;J614.6

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