能の謡分析のための深層学習によるブラインド音源分離を用いたメロディ抽出

The purpose of this study is to extract singing melody from mixed sounds related to Noh performances. Noh sounds include singing, accompaniments, and other elements. For analyzing Noh singing, we need singing solos, but they are hard to collect since there are only a few sources of solo passages. Therefore, we focus on the extraction of singing melody from mixtures of accompaniments and singing. In this paper, we demonstrate that source separation can be introduced as an efficient preprocessing step for Noh singing melody extraction. In addition, we compare melody extraction based on a convolutional neural network (CNN) and Long short-term memory (LSTM) approach with Melodia, a plug-in for melody extraction which is particularly accurate in the presence of music with wide fluctuations in pitch. Raw Pitch Accuracy and Overall Accuracy are introduced as evaluation metrics. Our experimental results show that it is efficient for melody extraction to introduce source separation. We also demonstrated that Deep learning-based melody estimation can be efficiently trained using singing after source separation