音樂生成在深度學習與音樂資訊檢索相關的研究中，是個涵蓋很廣的領域。
在本論文中，我們使用阿倫·福特於 1973 年在著作《無調性音樂結構》中發表的音類集理論方法，並提取其他樂曲特徵，包含節奏線與旋律線包絡，不僅增加了音樂及數學的理論基礎，同時也實現作曲時，了解在多種音樂曲風間各種音樂元素互相參照並轉化的目的。
我們提出的研究方法富具彈性，在不同情況，都能產生多樣性的組合，即使在資料量有限的情況下仍能進行操作。
生成的樂曲不僅有可聽性，從訓練好的分類模型預測結果中，也可以看出生成的樂曲在不同音樂因素組合間成功地轉換。

Music generation is a subject that is widely investigated in deep learning and MIR (Music Information Retrieval) research.
In this paper, we adopt Allen Forte’s Pitch-Class Set Theory from his book, The Structure of Atonal Music, 1973, and extract other musical features including rhythm patterns as well as melody contours. Not only do we enhance the theoretic foundation of music and mathematics, but also understand the reciprocal reference among music factors and different kinds of music styles while composing.
The method we propose is highly flexible. You can have a diversity of outcomes under various circumstances. Additionally, it can be applied with limited training data.
The music we generated is pleasant from subjective evaluation results, and successfully transformed into other styles from the results given by a pre-trained classifier model.

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