Name
Abstract | ||
|---|---|---|
In music informatics, there has been increasing attention to relative similarity as it plays a central role in music retrieval, recommendation, and musicology. Most approaches for relative similarity are based on distance metric learning, in which similarity relationship is modelled by a parameterised distance function. Normally, these parameters can be learned by solving a constrained optimisation problem using kernel-based methods. In this paper, we study the use of restricted Boltzmann machines (RBMs) in similarity modelling. We take advantage of RBM as a probabilistic neural network to assign a true hypothesis "x is more similar to y than to z" with a higher probability. Such model can be trained by maximising the true hypotheses while, at the same time, minimising the false hypotheses using a stochastic method. Alternatively, we show that learning similarity relations can be done deterministically by minimising the free energy function of a bipolar RBM or using a classification approach. In the experiments, we evaluate our proposed approaches on music scripts extracted from MagnaTagATune dataset. The results show that an energy-based optimisation approach with bipolar RBM can achieve better performance than other methods, including support vector machine and machine learning rank which are the state-of-the-art for this dataset. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1007/s00521-019-04106-y | NEURAL COMPUTING & APPLICATIONS |
Keywords | DocType | Volume |
Music similarity,Restricted Boltzmann machines,Machine learning | Journal | 32.0 |
Issue | ISSN | Citations |
8 | 0941-0643 | 0 |
PageRank | References | Authors |
0.34 | 13 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Son N. Tran | 1 | 1 | 1.70 |
| Son Ngo | 2 | 0 | 0.34 |
| Artur S. D'avila Garcez | 3 | 431 | 63.57 |