基于word2vec的旋律音符序列到音高的转换
From melodic note sequences to pitches using word2vec
AI总结：
本研究将word2vec技术应用于旋律，以2维语义空间和前2-4个音符为上下文预测音符，在儿童歌曲和巴赫奏鸣曲数据集上验证了音符语义向量与音高的复相关系数约0.80。
摘要：
将常用于语言建模的word2vec技术应用于旋律（将音符视为句子中的单词），能够捕获音高信息。本研究考察了两个数据集：20首儿童歌曲以及一段巴赫奏鸣曲节选。用于定义嵌入的语义空间维度极低，具体为2维。研究根据构成上下文的前2、3或4个音符来预测当前音符。对结果的多变量分析表明，代表音符的语义向量与其音高之间的复相关系数约为0.80。
英文摘要：
Applying the word2vec technique, commonly used in language modeling, to melodies, where notes are treated as words in sentences, enables the capture of pitch information. This study examines two datasets: 20 children's songs and an excerpt from a Bach sonata. The semantic space for defining the embeddings is of very small dimension, specifically 2. Notes are predicted based on the 2, 3 or 4 preceding notes that establish the context. A multivariate analysis of the results shows that the semantic vectors representing the notes have a multiple correlation coefficient of approximately 0.80 with their pitches.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
