【摘要】 针对音乐编曲来说手动编曲整首旋律相对较难，文章提出一自动音乐旋律补全模型，基于SVR和多头LSTM。编曲者提供短旋律片段，转换为音高序列，通过正反向SVR和多头LSTM进行补全，提供灵活性。不同运算量的模型可同时生成多个补全结果，为编曲者提供更多选择和修改的可能性。相较以往正向补全研究，引入反向补全和多头LSTM扩展了补全方式，准确率相较于传统单向LSTM提升11%。更多还原

【Abstract】 When it comes to music composition, manually crafting the entire melody can be relatively challenging.This paper proposes an automated music melody completion model based on Support Vector Regression(SVR)and Multi-Head Long Short-Term Memory(LSTM).Composers provide short melody fragments, which are transformed into pitch sequences.The completion process involves both forward and reverse SVR models, along with Multi-Head LSTM,introducing flexibility in the completion process.Models with varying computational complexities 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.更多还原