This article presents two methods to generate automatic improvisation using training over multidimensional sequences. We consider musical features such as melody, harmony, timbre, etc., as dimensions. We first present a system combining interpolated probabilistic models with a factor oracle. The probabilistic models are trained on a corpus of musical work to learn the correlation between dimensions, and they are used to guide the navigation in the factor oracle to ensure a logical improvisation. Improvisations are therefore created in a way in which the intuition of a context is enriched with multidimensional knowledge. We then introduce a system creating multidimensional improvisations based on communication between dimensions via probabilistic message passing. The communication infers some anticipatory behavior on each dimension influenced by the others, creating a consistent multidimensional improvisation. Both systems were evaluated by professional improvisers during listening sessions. Overall, the systems received good feedback and showed encouraging results—first, on how multidimensional knowledge can improve navigation in the factor oracle and, second, on how communication through message passing can emulate the interactivity between dimensions or musicians.

中文翻译：

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多维机器即兴的概率因素预言机

本文介绍了两种使用多维序列训练生成自动即兴演奏的方法。我们将旋律、和声、音色等音乐特征视为维度。我们首先提出了一个将内插概率模型与因子预言机相结合的系统。概率模型在音乐作品的语料库上进行训练，以了解维度之间的相关性，并用于指导因子预言机中的导航以确保逻辑即兴。因此，即兴创作的方式是用多维知识丰富对上下文的直觉。然后，我们介绍了一个系统，该系统通过概率消息传递基于维度之间的通信来创建多维即兴表演。沟通推断出每个维度上受其他维度影响的一些预期行为，从而创建一致的多维度即兴创作。两种系统都由专业的即兴演奏者在聆听过程中进行评估。总体而言，这些系统收到了良好的反馈并显示出令人鼓舞的结果——首先，关于多维知识如何改进因子预言机中的导航，其次，关于通过消息传递的通信如何模拟维度或音乐家之间的交互。