Multilayer feedforward networks are universal approximators
Neural Networks
Support vector machine learning for interdependent and structured output spaces
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Information-theoretic metric learning
Proceedings of the 24th international conference on Machine learning
Distance Metric Learning for Large Margin Nearest Neighbor Classification
The Journal of Machine Learning Research
From Low-Level to High-Level: Comparative Study of Music Similarity Measures
ISM '09 Proceedings of the 2009 11th IEEE International Symposium on Multimedia
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
Similarity adaptation in an exploratory retrieval scenario
AMR'10 Proceedings of the 8th international conference on Adaptive Multimedia Retrieval: context, exploration, and fusion
Context-aware music recommender systems: workshop keynote abstract
Proceedings of the 21st international conference companion on World Wide Web
Data gathering for a culture specific approach in MIR
Proceedings of the 21st international conference companion on World Wide Web
Adapting similarity on the MagnaTagATune database: effects of model and feature choices
Proceedings of the 21st international conference companion on World Wide Web
From region similarity to category discovery
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
An experimental comparison of similarity adaptation approaches
AMR'11 Proceedings of the 9th international conference on Adaptive Multimedia Retrieval: large-scale multimedia retrieval and evaluation
Combining sources of description for approximating music similarity ratings
AMR'11 Proceedings of the 9th international conference on Adaptive Multimedia Retrieval: large-scale multimedia retrieval and evaluation
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Computational modelling of music similarity is an increasingly important part of personalisation and optimisation in music information retrieval and research in music perception and cognition. The use of relative similarity ratings is a new and promising approach to modelling similarity that avoids well known problems with absolute ratings. In this article, we use relative ratings from the MagnaTagATune dataset with new and existing variants of state-of-the-art algorithms and provide the first comprehensive and rigorous evaluation of this approach. We compare metric learning based on support vector machines (SVMs) and metric-learning-to-rank (MLR), including a diagonal and a novel weighted variant, and relative distance learning with neural networks (RDNN). We further evaluate the effectiveness of different high and low level audio features and genre data, as well as dimensionality reduction methods, weighting of similarity ratings, and different sampling methods. Our results show that music similarity measures learnt on relative ratings can be significantly better than a standard Euclidian metric, depending on the choice of learning algorithm, feature sets and application scenario. MLR and SVM outperform DMLR and RDNN, while MLR with weighted ratings leads to no further performance gain. Timbral and music-structural features are most effective, and all features jointly are significantly better than any other combination of feature sets. Sharing audio clips (but not the similarity ratings) between test and training sets improves performance, in particular for the SVM-based methods, which is useful for some applications scenarios. A testing framework has been implemented in Matlab and made publicly available http://mi.soi.city.ac.uk/datasets/ir2012framework so that these results are reproducible.