用于动态序列合成的基于核密度估计的隐马尔科夫模型

Learning Kernel-Based HMMs for Dynamic Sequence Synthesis

提出了一种用于动态序列合成的统计模型———基于核密度估计的隐马尔可夫模型 .给定一个输入动态序列 ,该模型可以自动产生被控的输出动态序列 .文中提出的模型是一种以非参数化概率密度估计作为观测模型的隐马尔可夫模型 .该模型对输入和受控输出序列的联合概率分布进行建模 ,并利用基于核函数的概率密度估计来学习联合概率分布的细节信息 .文中详细地讨论了该模型的学习和合成算法 .并利用该模型实现了一个虚拟指挥系统 .即给定一段音乐 ,系统可以自动生成相关的乐队指挥动作 .该文利用该系统对不同风格和节拍的音乐做了实验 .实验结果验证了算法的有效性 .

We propose a statistic model - Kernel based Hidden Markov Model (K-HMM) for dynamic sequence synthesis. From an input sequence, the K-HMM can generate a controlled sequence automatically. A K-HMM is a HMM for which the non-parametric density estimation is used to model the state observation density of the joint input and output distribution. The subtle details of the joint distribution are well kept in our model. We describe the details of learning and synthesizing algorithm of K-HMM. By using a K-HMM, we propose a system that synthesizes a virtual conductor. From a given music sequence, virtual conductor generates a conducting gesture sequence automatically. We demonstrate our virtual conductor by synthesizing extensive animation sequences from input music sequences with different styles and beat patterns.