Abstract
We explore the task of computational beat tracking for musical audio signals from the perspective of putting an end-user directly in the processing loop. Unlike existing “semi-automatic” approaches for beat tracking, where users may select from among several possible outputs to determine the one that best suits their aims, in our approach we examine how high-level user input could guide the manner in which the analysis is performed. More specifically, we focus on the perceptual difficulty of tapping the beat, which has previously been associated with the musical properties of expressive timing and slow tempo. Since musical examples with these properties have been shown to be poorly addressed even by state of the art approaches to beat tracking, we re-parameterise an existing deep learning based approach to enable it to more reliably track highly expressive music. In a small-scale listening experiment we highlight two principal trends: i) that users are able to consistently disambiguate musical examples which are easy to tap to and those which are not; and in turn ii) that users preferred the beat tracking output of an expressive-parameterised system to the default parameterisation for highly expressive musical excerpts.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
- 1.
The probability of tempo changes varies exponentially with the negative of the “transition-\(\lambda \)”, thus higher values of this parameter favour constant tempo from one beat to the next one [20].
References
Andersen, K., Knees, P.: Conversations with expert users in music retrieval and research challenges for creative MIR. In: Proceedings of the 17th International Society for Music Information Retrieval Conference, pp. 122–128 (2016)
Böck, S., Davies, M.E.P., Knees, P.: Multi-task learning of tempo and beat: learning one to improve the other. In: Proceedings of the 20th International Society for Music Information Retrieval Conference, pp. 486–493 (2019)
Böck, S., Korzeniowski, F., Schlüter, J., Krebs, F., Widmer, G.: Madmom: a new python audio and music signal processing library. In: Proceedings of the 2016 ACM Multimedia Conference, pp. 1174–1178 (2016). https://doi.org/10.1145/2964284.2973795
Böck, S., Krebs, F., Widmer, G.: A multi-model approach to beat tracking considering heterogeneous music styles. In: Proceedings of the 15th International Society for Music Information Retrieval Conference, pp. 603–608 (2014)
Böck, S., Krebs, F., Widmer, G.: Joint beat and downbeat tracking with recurrent neural networks. In: Proceedings of the 17th International Society for Music Information Retrieval Conference, pp. 255–261 (2016)
Böck, S., Schedl, M.: Enhanced beat tracking with context-aware neural networks. In: Proceedings of the 14th International Conference on Digital Audio Effects, pp. 135–139 (2011)
Bååth, R., Madison, G.: The subjective difficulty of tapping to a slow beat. In: Proceedings of the 12th International Conference on Music Perception and Cognition, pp. 82–55 (2012)
Cannam, C., Landone, C., Sandler, M.: Sonic visualiser: an open source application for viewing, analysing, and annotating music audio files. In: Proceedings of the 18th ACM International Conference on Multimedia, pp. 1467–1468 (2010). https://doi.org/10.1145/1873951.1874248
Collins, N.: Towards a style-specific basis for computational beat tracking. In: Proceedings of the 9th International Conference on Music Perception and Cognition (ICMPC), pp. 461–467 (2006)
Davies, M.E.P., Böck, S.: Evaluating the evaluation measures for beat tracking. In: Proceedings of the 15th International Society for Music Information Retrieval Conference, pp. 637–642 (2014)
Desain, P., Honing, H.: Does expressive timing in music performance scale proportionally with tempo? Psychol. Res. 56(4), 285–292 (1994). https://doi.org/10.1007/BF00419658
Goto, M., Yoshii, K., Fujihara, H., Mauch, M., Nakano, T.: Songle: a web service for active music listening improved by user contributions. In: Proceedings of the 12th International Society for Music Information Retrieval Conference, pp. 311–316 (2011)
Grosche, P., Müller, M., Sapp, C.: What makes beat tracking difficult? A case study on chopin mazurkas. In: Proceedings of the 11th International Society for Music Information Retrieval Conference, pp. 649–654 (2010)
Hainsworth, S.: Techniques for the Automated Analysis of Musical Audio. Ph.D. thesis, University of Cambridge (2004)
Holzapfel, A., Davies, M.E.P., Zapata, J.R., Oliveira, J., Gouyon, F.: Selective sampling for beat tracking evaluation. IEEE Trans. Audio Speech Lang. Process. 20(9), 2539–2548 (2012). https://doi.org/10.1109/TASL.2012.2205244
Humphrey, E.J., Turnbull, D., Collins, T.: A brief review of creative MIR. In: Late-breaking Demo Session of the 14th International Society for Music Information Retrieval Conference (2013)
Iversen, J.R., Patel, A.D.: The Beat Alignment Test (BAT): surveying beat processing abilities in the general population. In: Proceedings of the 10th International Conference on Music Perception and Cognition, pp. 465–468 (2010)
Jin, C.T., Davies, M.E.P., Campisi, P.: Embedded systems feel the beat in new orleans: highlights from the IEEE signal processing cup 2017 student competition [SP Competitions]. IEEE Signal Process. Mag. 34(4), 143–170 (2017). https://doi.org/10.1109/MSP.2017.2698075
Konstan, J.A., Riedl, J.: Recommender systems: from algorithms to user experience. User Model. User-Adap. Inter. 22(1), 101–123 (2012). https://doi.org/10.1007/s11257-011-9112-x
Krebs, F., Böck, S., Widmer, G.: An efficient state space model for joint tempo and meter tracking. In: Proceedings of the 16th International Society for Music Information Retrieval Conference, pp. 72–78 (2015)
Moelants, D., McKinney, M.: Tempo perception and musical content: what makes a piece fast, slow or temporally ambiguous? In: Proceedings of the 8th International Conference on Music Perception and Cognition, pp. 558–562 (2004)
Serra, X., et al.: Roadmap for music information research (2013), Creative Commons BY-NC-ND 3.0 license, ISBN: 978-2-9540351-1-6
Stowell, D., Robertson, A., Bryan-Kinns, N., Plumbley, M.D.: Evaluation of live human-computer music-making: quantitative and qualitative approaches. Int. J. Hum. Comput. Stud. 67(11), 960–975 (2009). https://doi.org/10.1016/j.ijhcs.2009.05.007
Sturm, B.L.: Classification accuracy is not enough. J. Intell. Inf. Syst. 41(3), 371–406 (2013). https://doi.org/10.1007/s10844-013-0250-y
Urbano, J., Schedl, M., Serra, X.: Evaluation in music information retrieval. J. Intell. Inf. Syst. 41(3), 345–369 (2013). https://doi.org/10.1007/s10844-013-0249-4
Zapata, J.R., Holzapfel, A., Davies, M.E.P., Oliveira, J.L., Gouyon, F.: Assigning a confidence threshold on automatic beat annotation in large datasets. In: Proceedings of the 13th International Society for Music Information Retrieval Conference, pp. 157–162 (2012)
Acknowledgments
This work is supported by Portuguese National Funds through the FCT-Foundation for Science and Technology, I.P., under the grant SFRH/BD/120383/2016 and the project IF/01566/2015.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 Springer Nature Switzerland AG
About this paper
Cite this paper
Sá Pinto, A., Davies, M.E.P. (2021). Tapping Along to the Difficult Ones: Leveraging User-Input for Beat Tracking in Highly Expressive Musical Content. In: Kronland-Martinet, R., Ystad, S., Aramaki, M. (eds) Perception, Representations, Image, Sound, Music. CMMR 2019. Lecture Notes in Computer Science(), vol 12631. Springer, Cham. https://doi.org/10.1007/978-3-030-70210-6_5
Download citation
DOI: https://doi.org/10.1007/978-3-030-70210-6_5
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-70209-0
Online ISBN: 978-3-030-70210-6
eBook Packages: Computer ScienceComputer Science (R0)