论文标题
素描表达：通过自我监督学习的富有表现力的钢琴表演的灵活渲染
Sketching the Expression: Flexible Rendering of Expressive Piano Performance with Self-Supervised Learning
论文作者
论文摘要
我们提出了一个系统，以灵活的音乐表达方式呈现象征性的钢琴表演。有必要积极控制音乐表达，以创建传达各种情感或细微差别的新音乐表演。但是，以前的方法仅限于遵循作曲家的音乐表达准则或仅处理音乐属性的一部分。我们的目标是使用条件VAE框架来消除钢琴性能的整个音乐表达和结构属性。它随机地从潜在表示和给定的注释结构中生成表达参数。此外，我们采用自我监督的方法，迫使潜在变量表示目标属性。最后，我们利用一个两步编码器和解码器，学习层次依赖性以增强产出的自然性。实验结果表明，我们的系统可以稳定地生成与给定的音乐分数相关的性能参数，学习分离的表示形式，并彼此独立地控制音乐属性。
We propose a system for rendering a symbolic piano performance with flexible musical expression. It is necessary to actively control musical expression for creating a new music performance that conveys various emotions or nuances. However, previous approaches were limited to following the composer's guidelines of musical expression or dealing with only a part of the musical attributes. We aim to disentangle the entire musical expression and structural attribute of piano performance using a conditional VAE framework. It stochastically generates expressive parameters from latent representations and given note structures. In addition, we employ self-supervised approaches that force the latent variables to represent target attributes. Finally, we leverage a two-step encoder and decoder that learn hierarchical dependency to enhance the naturalness of the output. Experimental results show that our system can stably generate performance parameters relevant to the given musical scores, learn disentangled representations, and control musical attributes independently of each other.
