Classically, the style of the generated music by deep learning models is usually governed by the training dataset. In this article, we improved this by proposing the continuous style embedding ${z}_{s}$ to the general formulation of variational autoencoder (VAE) to allow users to be able to condition on the style of the generated music. For this purpose, we explored and compared two different methods to integrate ${z}_{s}$ into the VAE. In the literature of conditional generative modeling, disentanglement of attributes from the latent space is often associated with better generative performance. In our experiments, we find that this is not the case with our proposed model. Empirically and from a musical theory perspective, we show that our proposed model can generate better music samples than a baseline model that utilizes a discrete style label. The source code and generated samples are available at https://github.com/daQuincy/DeepMusicvStyle .

中文翻译：

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ClaviNet：生成具有不同音乐风格的音乐

传统上，深度学习模型生成的音乐的样式通常由训练数据集控制。在本文中，我们通过建议连续样式嵌入来对此进行了改进$ {z} _ {s} $可变自动编码器（VAE）的一般格式，使用户能够根据所生成音乐的风格进行调整。为此，我们探索并比较了两种不同的集成方法$ {z} _ {s} $进入VAE。在条件生成建模的文献中，属性与潜在空间的纠缠通常与更好的生成性能相关。在我们的实验中，我们发现建议的模型并非如此。从音乐理论的角度，从经验上讲，我们表明，与利用离散样式标签的基线模型相比，我们提出的模型可以生成更好的音乐样本。源代码和生成的样本可在以下位置获得：https://github.com/daQuincy/DeepMusicvStyle 。