Learning Contextualized Music Semantics from Tags via a Siamese Network
This addresses the out-of-vocabulary problem in music information retrieval for researchers and practitioners, though it appears incremental as it builds on existing Siamese network and topic model techniques.
The paper tackles the challenge of modeling contextualized musical concepts from co-occurring tags in music information retrieval, and shows that their Siamese network approach outperforms state-of-the-art methods in semantic priming and music tag completion on three public datasets.
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.