论文标题
与置换不变语言模型的交响曲生成
Symphony Generation with Permutation Invariant Language Model
论文作者
论文摘要
在这项工作中，我们提出了一个置换不变的语言模型Symphonynet，作为象征性交响音乐生成的解决方案。我们建议使用基于变压器的自动回归语言模型具有特定的3-D位置嵌入的新型多通道可重复的多功能仪表（MMR）表示，并模拟音乐序列。为了克服长度溢出在建模超长的交响令牌时，我们还提出了一个经过修改的字节对编码算法（音乐bpe），以用于音乐令牌，并引入一种新颖的线性变压器解码器架构作为骨干。同时，我们通过从输入中掩盖仪器信息来训练解码器将自动编排作为联合任务学习。我们还引入了一个大规模的符号交响数据集，以实现交响曲生成研究的发展。经验结果表明，所提出的方法可以产生连贯，新颖，复杂且和谐的交响曲，作为多轨多磁场符号音乐生成的先驱解决方案。
In this work, we propose a permutation invariant language model, SymphonyNet, as a solution for symbolic symphony music generation. We propose a novel Multi-track Multi-instrument Repeatable (MMR) representation for symphonic music and model the music sequence using a Transformer-based auto-regressive language model with specific 3-D positional embedding. To overcome length overflow when modeling extra-long symphony tokens, we also propose a modified Byte Pair Encoding algorithm (Music BPE) for music tokens and introduce a novel linear transformer decoder architecture as a backbone. Meanwhile, we train the decoder to learn automatic orchestration as a joint task by masking instrument information from the input. We also introduce a large-scale symbolic symphony dataset for the advance of symphony generation research. Empirical results show that the proposed approach can generate coherent, novel, complex and harmonious symphony as a pioneer solution for multi-track multi-instrument symbolic music generation.
