论文标题
对马尔可夫逻辑网络在音乐信号分析中的适用性进行批判性查看
A Critical Look at the Applicability of Markov Logic Networks for Music Signal Analysis
论文作者
论文摘要
近年来，Markov Logic网络（MLN）被认为是用于音乐信号分析的潜在有用的范例。由于所有隐藏的马尔可夫模型都可以重新重新汇总为MLN，因此后者可以提供一个无所不包的框架，该框架重新使用并扩展了该领域的先前工作。但是，仅仅因为理论上有可能将以前的工作重新升级为MLN，这并不意味着它是有利的。在本文中，我们分析了一些提议的MLN示例用于音乐分析，并考虑了与（动态）贝叶斯网络相同的音乐依赖关系相比，考虑了它们的实际缺点。我们认为，许多实用的障碍，例如缺乏对序列的支持和任意连续概率分布的支持，这使得MLN因其所需的推理算法而导致的易于制定和计算需求而言，对于拟议的音乐应用而言，MLN的理想程度不足。这些结论并非特定于音乐，而是适用于其他领域，尤其是在涉及连续观察的顺序数据时。最后，我们表明，在（动态）贝叶斯网络的更常用框架中，提出的示例的基础思想可以很好地表达。
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.
