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Learning Contextualized Music Semantics from Tags via a Siamese Neural Network
ABSTRACT
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.
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