Abstract
This paper describes an analyzer that simultaneously learns grouping and metrical structures on the basis of the generative theory of tonal music (GTTM) by using a deep learning technique. GTTM is composed of four modules that are in series. GTTM has a feedback loop in which the former module uses the result of the latter module. However, as each module has been independent in previous GTTM analyzers, they did not form a feedback loop. For example, deepGTTM-I and deepGTTM-II independently learn grouping and metrical structures by using a deep learning technique. In light of this, we present deepGTTM-III, which is a new analyzer that includes the concept of feedback that enables simultaneous learning of grouping and metrical structures by integrating both deepGTTM-I and deepGTTM-II networks. The experimental results revealed that deepGTTM-III outperformed deepGTTM-I and had similar performance to deepGTTM-II.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Lerdahl, F., Jackendoff, R.: A Generative Theory of Tonal Music. MIT Press, Cambridge (1985)
Hirata, K., Hiraga, R.: Ha-Hi-Hun plays Chopin’s Etude. In: Working Notes of IJCAI-03 Workshop on Methods for Automatic Music Performance and Their Applications in a Public Rendering Contest (2003)
Hirata, K., Matsuda, S., Kaji, K., Nagao, K.: Annotated music for retrieval, reproduction, and sharing. In: Proceedings of the 2004 International Computer Music Conference (ICMC 2004), pp. 584–587 (2004)
Hirata, K., Matsuda, S.: Interactive music summarization based on GTTM. In: Proceedings of the 2002 International Society for Music Information Retrieval Conference (ISMIR 2002), pp. 86–93 (2002)
Hamanaka, M., Hirata, K., Tojo, S.: Melody morphing method based on GTTM. In: Proceedings of the 2008 International Computer Music Conference (ICMC 2008), pp. 155–158 (2008)
Hamanaka, M., Hirata, K., Tojo, S.: Melody extrapolation in GTTM approach. In: Proceedings of the 2009 International Computer Music Conference (ICMC 2009), pp. 89–92 (2009)
Hamanaka, M., Hirata, K., Tojo, S.: Implementing ‘a generative theory of tonal music’. J. New Music Res. 35(4), 249–277 (2006)
Hamanaka, M., Hirata, K., Tojo, S.: FATTA: full automatic time-span tree analyzer. In: Proceedings of the 2007 International Computer Music Conference (ICMC 2007), pp. 153–156 (2007)
Miura, Y., Hamanaka, M., Hirata, K., Tojo, S.: Decision tree to detect GTTM group boundaries. In: Proceedings of the 2009 International Computer Music Conference (ICMC 2009), pp. 125–128 (2009)
Kanamori, K., Hamanaka, M.: Method to detect GTTM local grouping boundaries based on clustering and statistical learning. In: Proceedings of the 2014 International Computer Music Conference (ICMC 2014), pp. 125–128 (2014)
Hamanaka, M., Hirata, K., Tojo, S.: \(sigma\)GTTM III: learning-based time-span tree generator based on PCFG. In: Proceedings of the 11th International Symposium on Computer Music Multidisciplinary Research (CMMR 2015), pp. 303–317 (2015)
Hamanaka, M., Hirata, K., Tojo, S.: Musical structural analysis database based on GTTM. In: Proceedings of the 2014 International Society for Music Information Retrieval Conference (ISMIR 2014), pp. 325–330 (2014)
Hamanaka, M., Hirata, K., Tojo, S.: deepGTTM-I: local boundary analyzer based on a deep learning technique. In: Proceedings of the 12th International Symposium on Computer Music Multidisciplinary Research (CMMR 2016), pp. 8–20 (2016)
Hamanaka, M., Hirata, K., Tojo, S.: deepGTTM-II: automatic generation of metrical structure based on deep learning technique. In: Proceedings of 13th Sound and Music Computing Conference (SMC 2016), pp. 203–210 (2016)
Choi, K., Fazekas, G., Sandler, M.: Automatic tagging using deep convolutional neural networks. In: Proceedings of the 2016 International Society for Music Information Retrieval Conference (ISMIR 2016), pp. 805–811 (2016)
Zhou, X., Lerch, A.: Chord detection using deep learning. In: Proceedings of the 2015 International Society for Music Information Retrieval Conference (ISMIR 2015), pp. 52–58 (2015)
Deng, J., Kwok, Y.: Hybrid Gaussian-HMM-deep learning approach for automatic chord estimation with very large vocabulary. In: Proceedings of the 2016 International Society for Music Information Retrieval Conference (ISMIR 2016), pp. 812–818 (2016)
Oord, A., Sander, D., Benjamin, S.: Deep content-based music recommendation. In: Proceedings of the Advances in Neural Information Processing Systems 26 (NIPS 2013), pp. 2643–2651 (2013)
Sigtia, S., Benetos, E., Dixon, S.: An end-to-end neural network for polyphonic piano music transcription. IEEE/ACM Trans. Audio Speech Lang. Process. (TASLP) 24(5), 927–939 (2016)
Hinton, G.E., Osindero, S., Teh, Y.W.: A fast learning algorithm for deep belief nets. Neural Comp. 18, 1527–1554 (2006)
MakeMusic Inc., “Finale” (2018). http://www.finalemusic.com/
Acknowledgments
This work was supported by JSPS KAKENHI Grant Numbers 17H01847, 25700036, 16H01744, and 23500145.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer Nature Switzerland AG
About this paper
Cite this paper
Hamanaka, M., Hirata, K., Tojo, S. (2018). deepGTTM-III: Multi-task Learning with Grouping and Metrical Structures. In: Aramaki, M., Davies , M., Kronland-Martinet, R., Ystad, S. (eds) Music Technology with Swing. CMMR 2017. Lecture Notes in Computer Science(), vol 11265. Springer, Cham. https://doi.org/10.1007/978-3-030-01692-0_17
Download citation
DOI: https://doi.org/10.1007/978-3-030-01692-0_17
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-01691-3
Online ISBN: 978-3-030-01692-0
eBook Packages: Computer ScienceComputer Science (R0)