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.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
Notes
References
Bar-Hillel A, Hertz T, Shental N, Weinshall D (2003) Learning distance functions using equivalence relations. In: ICML, pp 11–18
Bilenko M, Mooney RJ (2003) Adaptive duplicate detection using learnable string similarity measures. In: Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining, KDD ’03. ACM, New York, NY, USA, pp 39–48. https://doi.org/10.1145/956750.956759
Bromley J, Guyon I, LeCun Y, Säckinger E, Shah R (1993) Signature verification using a “siamese” time delay neural network. In: Proceedings of the 6th international conference on neural information processing systems, NIPS’93, pp 737–744
Carreira-Perpinan MA, Hinton GE (2005) On contrastive divergence learning. In: Proceedings of the tenth international workshop on artificial intelligence and statistics, pp 33–40
Chechik G, Sharma V, Shalit U, Bengio S (2010) Large scale online learning of image similarity through ranking. J Mach Learn Res 11:1109–1135. http://dl.acm.org/citation.cfm?id=1756006.1756042
Cherla S, Tran SN, d’Avila Garcez AS, Weyde T (2017) Generalising the discriminative restricted Boltzmann machines. In: Artificial neural networks and machine learning—ICANN 2017—26th international conference on artificial neural networks, pp 111–119
Dempster AP, Laird NM, Rubin DB (1977) Maximum likelihood from incomplete data via the EM algorithm. J R Stat Soc Ser B 39:1–38. http://web.mit.edu/6.435/www/Dempster77.pdf
Frome A, Singer Y, Sha F, Malik J (2007) Learning globally-consistent local distance functions for shape-based image retrieval and classification. In: ICCV, pp 1–8
Hinton GE (2002) Training products of experts by minimizing contrastive divergence. Neural Comput 14(8):1771–1800. https://doi.org/10.1162/089976602760128018
Hoffer E, Ailon N (2015) Deep metric learning using triplet network. In: Feragen A, Pelillo M, Loog M (eds) SIMBAD, lecture notes in computer science, vol 9370. Springer, pp 84–92
Hu N, Dannenberg RB, Lewis AL (2002) A probabilistic model of melodic similarity. In: International computer music conference (ICMC). 2002. The International Computer Music Association, Goteborg, Sweden
Huang A (2008) Similarity measures for text document clustering, pp 49–56
Jain P, Kulis B, Dhillon IS, Grauman K (2008) Online metric learning and fast similarity search. In: Koller D, Schuurmans D, Bengio Y, Bottou L (eds) NIPS. Curran Associates, Inc., pp 761–768
Larochelle H, Bengio Y (2008) Classification using discriminative restricted Boltzmann machines. In: Proceedings of the 25th international conference on machine learning, ICML ’08. ACM, New York, NY, USA, pp 536–543. https://doi.org/10.1145/1390156.1390224
McFee B, Lanckriet GRG (2010) Metric learning to rank. In: ICML, pp 775–782
Moghaddam B, Nastar C, Pentland A (1996) A bayesian similarity measure for direct image matching. In: Proceedings of the 13th international conference on pattern recognition—vol 2, ICPR ’96. IEEE Computer Society, Washington, DC, USA, p 350. https://doi.org/10.1109/ICPR.1996.546848
Pelckmans K, De Brabanter J, Suykens JAK, De Moor B (2005) Handling missing values in support vector machine classifiers. Neural Netw 18(5–6):684–692. https://doi.org/10.1016/j.neunet.2005.06.025
Salakhutdinov R, Mnih A, Hinton G (2007) Restricted Boltzmann machines for collaborative filtering. In: Machine learning, proceedings of the twenty-fourth international conference (ICML 2004). ACM, AAAI Press, pp 791–798
Schultz M, Joachims T (2004) Learning a distance metric from relative comparisons. In: Thrun S, Saul L, Schölkopf B (eds) Advances in neural information processing systems, vol 16. MIT Press, Cambridge
Serra X (2012) Data gathering for a culture specific approach in mir. In: Proceedings of the 21st international conference on World Wide Web, WWW ’12 companion. ACM, New York, NY, USA, pp 867–868. https://doi.org/10.1145/2187980.2188216
Smolensky P (1986) Information processing in dynamical systems: foundations of harmony theory. In: Rumelhart DE, McClelland JL (eds) Parallel distributed processing. Foundations, vol 1. MIT Press, Cambridge, pp 194–281
Stober S, Nurnberger A (2011) An experimental comparison of similarity adaptation approaches. In: Proceedings of the AMR-11
Tran SN, Wolff D, Weyde T, d’Avila Garcez AS (2014) Feature preprocessing with restricted Boltzmann machines for music similarity learning. In: AES international conference on semantic audio 2014, London, UK, 27–29 Jan 2014
Weinberger KQ, Saul LK (2009) Distance metric learning for large margin nearest neighbor classification. J Mach Learn Res 10:207–244. http://dl.acm.org/citation.cfm?id=1577069.1577078
Wolff D, Stober S, Nurnberger A, Weyde T (2012) A systematic comparison of music similarity adaptation approaches. In: 13th international conference on music information retrieval (ISMIR’12)
Wolff D, Weyde T (2011) Adapting metrics for music similarity using comparative ratings. In: Proceedings of the 12th international society for music information retrieval conference. Miami (Florida), USA, pp 73–78. http://ismir2011.ismir.net/papers/PS1-6.pdf
Wolff D, Weyde T (2014) Learning music similarity from relative user ratings. Inf Retr 17(2):109–136
Acknowledgements
We thank the Machine Learning and Music Informatics Research Group at City, University of London for the dataset used in our experiments.
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of interest
The authors declare that they have no conflict of interest.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Tran, S.N., Ngo, S. & Garcez, A.d. Probabilistic approaches for music similarity using restricted Boltzmann machines. Neural Comput & Applic 32, 3999–4008 (2020). https://doi.org/10.1007/s00521-019-04106-y
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00521-019-04106-y