  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.
