We examine the problem of learning a probabilistic model for melody directly from musical sequences belonging to the same genre. This is a challenging task as one needs to capture not only the rich temporal structure evident in music, but also the complex statistical dependencies among different music components. To address this problem we introduce the Variable-gram Topic Model, which couples the latent topic formalism with a systematic model for contextual information. We evaluate the model on next-step prediction. Additionally, we present a novel way of model evaluation, where we directly compare model samples with data sequences using the Maximum Mean Discrepancy of string kernels, to assess how close is the model distribution to the data distribution. We show that the model has the highest performance under both evaluation measures when compared to LDA, the Topic Bigram and related non-topic models.
WuYun: Exploring hierarchical skeleton-guided melody generation using knowledge-enhanced deep learning
Zhejiang University（浙江大学）；Alibaba-Zhejiang University Joint Institute of Frontier Technologies（阿里巴巴-浙江大学前沿技术联合研究院）；Innovation Center of Yangtze River Delta（长三角创新中心）；Microsoft Research Asia（微软亚洲研究院）
Beyond Topics: Discovering Latent Healthcare Objectives from Event Sequences
Australian Artificial Intelligence Institute（澳大利亚人工智能研究所）；University of Technology Sydney（悉尼科技大学）；The Children’s Cancer Research Unit（儿童癌症研究组）；The Children’s Hospital at Westmead（韦斯特米德儿童医院）
