批判性地研究马尔可夫逻辑网络在音乐信号分析中的适用性（cs AI）

• 2020 年 1 月 21 日
• 筆記

近年来，已提出马尔可夫逻辑网络（MLN）作为音乐信号分析的潜在有用范例。因为所有隐藏的Markov模型都可以重新构造为MLN，所以MLN可以提供一个包罗万象的框架，可以重用和扩展该领域的先前工作。但是，仅因为理论上有可能将以前的工作重新编写为MLN，并不意味着它是有利的。在本文中，我们分析了一些提出的用于音乐分析的MLN示例，并与与（动态）贝叶斯网络建立相同的音乐依赖关系进行比较时考虑了它们的实际缺点。我们认为，许多实际的障碍，例如缺乏对序列的支持和对任意连续概率分布的支持，使得MLN对于拟议的音乐应用而言，由于其所需的推理算法而在表达容易和计算要求方面均不理想。这些结论并非特定于音乐，而是也适用于其他领域，尤其是当涉及到连续观察的连续数据时。最后，我们表明，所提出示例的基本思想可以在（动态）贝叶斯网络的更常用框架中很好地表达。

原文标题:A Critical Look at the Applicability of Markov Logic Networks for Music Signal Analysis

原文：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.

原文作者：Johan Pauwels, György Fazekas, Mark B. Sandler

原文链接：https://arxiv.org/abs/2001.06086