Based on Neural Network and Vector Regression Melody Fragment Comple-tion Algorithm
When it comes to music composition,manually crafting the entire melody can be rela-tively challenging.This paper proposes an automated music melody completion model based on Support Vector Regression(SVR)and Multi-Head Long Short-Term Memory(LSTM).Com-posers provide short melody fragments,which are transformed into pitch sequences.The com-pletion process involves both forward and reverse SVR models,along with Multi-Head LSTM,introducing flexibility in the completion process.Models with varying computational complexi-ties can simultaneously generate multiple completion results,offering composers a broader range of choices and modification possibilities.In comparison to previous research focused solely on forward completion,the incorporation of reverse completion and Multi-Head LSTM expands the completion methodology,resulting in an 11％improvement in accuracy over traditional one-way LSTM approaches.
intelligent compositionartificial intelligenceneural networksymbolic music
NETL
NSTL
万方数据
