運動音樂風格之自動作曲系統

Abstract

本論文基於自動作曲科技技術日漸進步，且公眾越來越重視運動維持身體健康，為使公眾能無負擔版權費用與輕鬆運動，而發展出一套運動音樂風格自動作曲系統，該系統由運用一階馬可夫鏈完成的和弦行進產生器、基於遞迴式類神經網絡模型的高音部產生器、由鼓組資料庫組成的鼓組節奏產生器與結合一階馬可夫鏈產生曲式結構與公式化規則之伴奏方式的結構產生器以上四個部分組成。實驗中讓9位受測者於有氧運動狀態下聆聽白雜訊與運動音樂風格自動作曲系統之樂曲，結果聆聽運動音樂風格自動作曲系統之樂曲時的運動過程心理狀態較聆聽白雜訊時感到輕鬆(p<0.05)。

The technologies of algorithmic composition have made a good progress in the recent days, and the public is more and more paying attentions on exercise and health. This thesis develops a sport music style algorithmic composition system in order to reduce the fees of copyright and fatigues of exercise. The system contains a chord progression generator fulfilled with first-order Markov chain, an upper register part generator based on recurrent neural network model, a corpus-based drum set rhythm pattern generator, and a structure form generator performed with first-order Markov chain model with formulized accompaniment. The 9 subjects listening to music made by the sport music style algorithmic composition system feels more relaxed than listening to white noise during the aerobic exercise (p<0.05).