智能系统学报2023，Vol.18Issue(6)：1287-1294,8.DOI:10.11992/tis.202303018
结合深度乐谱特征融合的钢琴指法生成方法
Piano fingering generation with deep musical score feature fusion
摘要
Abstract
Fingering is a key technique in piano playing.However,most musical scores have no finger notation except in beginners'textbooks.The HMM and LSTM models used for automatic piano fingering only model pitch information and ignore speed information,which will influence the fingering.This condition results in insufficient extraction of comprehensive features and a low accuracy rate for generated fingerings.A feature extraction method was first de-signed using the pitch and speed information of the musical score simultaneously to address these problems.The Word2Vec-CBOW model was then introduced to produce a fused feature vector.Further,data enhancement and joint training of left and right hand sequences were conducted on the original data according to the mirror symmetric charac-teristics of human left and right hands.Finally,the generation of fingering was realized by combining the bidirectional long short-term memory network-conditional random field(BiLSTM-CRF)model.Experimental results show that the proposed algorithm is considerably better than commonly used statistical and deep learning methods,which confirms the rationality and effectiveness of the proposed model.关键词
人工智能/音乐/信息检索/长短时记忆/循环神经网络/数据处理/特征提取/时间序列Key words
artificial intelligence/music/information retrieval/long short-term memory/recurrent neural networks/data processing/feature extraction/time series分类
信息技术与安全科学引用本文复制引用
李锵,吴正彪,关欣..结合深度乐谱特征融合的钢琴指法生成方法[J].智能系统学报,2023,18(6):1287-1294,8.基金项目
国家自然科学基金项目(61872267) （61872267）
天津市自然科学基金项目(16JCZDJC31100) （16JCZDJC31100）
天津大学创新基金项目(2021XZC-0024). （2021XZC-0024）
