過去自動作曲的研究，演算法產生出來的音樂作品並不完全符合音樂理論規則。本篇論文提出的方法為，以流行歌曲中重複率高的副歌旋律作為訓練對象，利用模糊類神經網路(fuzzy neural network, FNN)來產生新的音樂旋律。FNN是使用模糊倒傳遞演算法(fuzzy back-propagation algorithm)，模糊推論可模擬作曲家在創作過程中做決策的概念。產生出來的音樂旋律再以檢查調號(key signature)及協合音程(consonance interval)的方式來調整音符，以致其符合音樂理論。最後模擬的結果顯示，本論文提出的學習演算法擁有良好的學習能力及不錯的表現。The generated music from automatic music composition is not completely match the rule of music theory in the past research. This thesis proposed using fuzzy neural network (FNN) to training a repeating pattern melody which called refrain in pop music. A refrain usually repeats many times in the music objects. The proposed learning algorithm is based on fuzzy back propagation algorithm (FBP). The main goal of a fuzzy inference system is to model composer decision making within conceptual as the process of composing music. The music theory knowledge of consonance intervals and key signature were adopted to check and adjust the output melody to prevent incorrectly. The simulation results show that the proposed learning algorithm have a good learning ability and well performance.
