2020/01/16 by Johan Pauwels, Pauwels, Johan, György Fazekas +4
Computer Science · Engineering · Neuroscience · #Artificial Intelligence (cs.AI) #Audio and Speech Processing (eess.AS) #FOS: Computer and information sciences #FOS: Electrical engineering #Information Retrieval (cs.IR) #Machine Learning (cs.LG) #Music Technology and Sound Studies #Music and Audio Processing #Neural Networks and Applications #Neuroscience and Music Perception #Sound (cs.SD) #Time Series Analysis and Forecasting #cs.AI #cs.IR #cs.LG #cs.SD #eess.AS #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.2001.06086
Accepted for presentation at the Ninth International Workshop on Statistical Relational AI (StarAI 2020) at the 34th AAAI Conference on Artificial Intelligence (AAAI) in New York, on February 7th 2020
arxiv created 2020/01/16 · openalex publication_date 2020/01/16 · arxiv updated 2020/01/20 · openalex created_date 2022/07/26 · openalex updated_date 2026/07/28
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.