# Learning Contextualized Music Semantics from Tags via a Siamese Network

arXiv: https://arxiv.org/abs/1504.07968 · HTML: https://mlsanity.com/paper/1504.07968/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Apr 2015.

## Built on by (most cited first, 1 of 1)

- [An experimental evaluation of Siamese Neural Networks for robot localization using omnidirectional imaging in indoor environments](https://mlsanity.com/paper/2407.10536/) (2024)

## Abstract (arXiv)

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.