马尔可夫逻辑网络在音乐信号分析中的应用
A Critical Look at the Applicability of Markov Logic Networks for Music Signal Analysis
摘要：
近年来，马尔可夫逻辑网络（MLN）已被提出作为一个潜在的有用的范例，音乐信号分析。因为所有的隐马尔可夫模型都可以重新表述为MLN，后者可以提供一个全面的框架，重用和扩展该领域以前的工作。然而，仅仅因为理论上可以将以前的工作重新表述为MLN，并不意味着它是有利的。在本文中，我们分析了一些建议的MLNs的音乐分析的例子，并考虑其实际的缺点相比，制定相同的音乐依赖关系（动态）贝叶斯网络。我们认为，一些实际的障碍，如缺乏对序列和任意连续概率分布的支持，使MLN不太理想的建议的音乐应用程序，无论是在易于制定和计算的要求，由于其所需的推理算法。这些结论并不局限于音乐，但也适用于其他领域，特别是当涉及连续观察的序列数据时。最后，我们表明，所提出的例子背后的想法可以很好地表达在更常用的框架（动态）贝叶斯网络。
英文摘要：
In recent years, Markov logic networks (MLNs) have been proposed as a potentially useful paradigm for music signal analysis. Because all hidden Markov models can be reformulated as MLNs, the latter can provide an all-encompassing framework that reuses and extends previous work in the field. However, just because it is theoretically possible to reformulate previous work as MLNs, does not mean that it is advantageous. In this paper, we analyse some proposed examples of MLNs for musical analysis and consider their practical disadvantages when compared to formulating the same musical dependence relationships as (dynamic) Bayesian networks. We argue that a number of practical hurdles such as the lack of support for sequences and for arbitrary continuous probability distributions make MLNs less than ideal for the proposed musical applications, both in terms of easy of formulation and computational requirements due to their required inference algorithms. These conclusions are not specific to music, but apply to other fields as well, especially when sequential data with continuous observations is involved. Finally, we show that the ideas underlying the proposed examples can be expressed perfectly well in the more commonly used framework of (dynamic) Bayesian networks.
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