论文标题
具有人类贝叶斯优化的生成旋律组成
Generative Melody Composition with Human-in-the-Loop Bayesian Optimization
论文作者
论文摘要
深层生成模型即使是新手作曲家也可以通过采样潜在向量来生成各种旋律。但是，由于潜在空间不直觉且高维是由于发现所需的旋律是具有挑战性的。在这项工作中，我们提出了一个交互式系统，该系统支持具有人类贝叶斯贝叶斯优化（BO）的生成旋律组成。该系统采用了混合定位的方法。该系统生成候选旋律来评估，用户对它们进行评估并提供优先的反馈（即，在候选人中选择最佳的旋律）。此过程是根据BO技术迭代执行的，直到用户找到所需的旋律。我们使用我们的原型系统进行了一项试点研究，这表明了这种方法的潜力。
Deep generative models allow even novice composers to generate various melodies by sampling latent vectors. However, finding the desired melody is challenging since the latent space is unintuitive and high-dimensional. In this work, we present an interactive system that supports generative melody composition with human-in-the-loop Bayesian optimization (BO). This system takes a mixed-initiative approach; the system generates candidate melodies to evaluate, and the user evaluates them and provides preferential feedback (i.e., picking the best melody among the candidates) to the system. This process is iteratively performed based on BO techniques until the user finds the desired melody. We conducted a pilot study using our prototype system, suggesting the potential of this approach.
