用于速度跟踪和节奏Quantization的Monte Carlo方法
Monte Carlo Methods for Tempo Tracking and Rhythm Quantization
摘要：
我们提出了一个概率生成模型表达音乐表演的时间偏差。该模型的结构等价于一个开关状态空间模型。开关变量对应于乐谱中的离散音符位置。连续隐变量表示速度。我们制定了两个著名的音乐识别问题，即节奏跟踪和自动转录（节奏量化）过滤和最大后验（MAP）状态估计任务。精确计算后验特征，如MAP状态是棘手的，在这个模型类，所以我们引入蒙特卡罗方法的集成和优化。我们比较了马尔可夫链蒙特卡罗（MCMC）方法（如吉布斯抽样，模拟退火和迭代改进）和序列蒙特卡罗方法（粒子滤波）。我们的模拟结果表明，更好的结果与顺序的方法。该方法可以应用于在线和批量场景中，例如节奏跟踪和转录，因此在许多音乐应用中可能是有用的，例如自适应自动伴奏、乐谱排版和音乐信息检索。
英文摘要：
We present a probabilistic generative model for timing deviations in expressive music performance. The structure of the proposed model is equivalent to a switching state space model. The switch variables correspond to discrete note locations as in a musical score. The continuous hidden variables denote the tempo. We formulate two well known music recognition problems, namely tempo tracking and automatic transcription (rhythm quantization) as filtering and maximum a posteriori (MAP) state estimation tasks. Exact computation of posterior features such as the MAP state is intractable in this model class, so we introduce Monte Carlo methods for integration and optimization. We compare Markov Chain Monte Carlo (MCMC) methods (such as Gibbs sampling, simulated annealing and iterative improvement) and sequential Monte Carlo methods (particle filters). Our simulation results suggest better results with sequential methods. The methods can be applied in both online and batch scenarios such as tempo tracking and transcription and are thus potentially useful in a number of music applications such as adaptive automatic accompaniment, score typesetting and music information retrieval.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
