Music information retrieval faces a challenge in modeling contextualized
musical concepts formulated by a set of co-occurring tags. In this paper, we
investigate the suitability of our recently proposed approach based on a
Siamese neural network in fighting off this challenge. By means of tag features
and probabilistic topic models, the network captures contextualized semantics
from tags via unsupervised learning. This leads to a distributed semantics
space and a potential solution to the out of vocabulary problem which has yet
to be sufficiently addressed. We explore the nature of the resultant
music-based semantics and address computational needs. We conduct experiments
on three public music tag collections -namely, CAL500, MagTag5K and Million
Song Dataset- and compare our approach to a number of state-of-the-art
semantics learning approaches. Comparative results suggest that this approach
outperforms previous approaches in terms of semantic priming and music tag
completion.Comment: 20 pages. To appear in ACM TIST: Intelligent Music Systems and
Application
