Abstract
Drum fills are essential in the drummer’s playing. They regularly restore energy and announce the transition to a new part of the song. This aspect of the drums has not been explored much in the field of MIR because of the lack of datasets with drum fills labels. In this paper, we propose two methods to detect drum fills along a song, to obtain drum fills context information. The first method is a logistic regression which uses velocity-related handcrafted data and features from the latent space of a variational autoencoder. We give an analysis of the classifier performance regarding each features group. The second method, rule-based, considers a bar as a fill when a sufficient difference of notes is detected with respect to the adjacent bars. We use these two methods to extract regular pattern/ drum fill couples in a big dataset and examine the extraction result with plots and statistical test. In a second part, we propose a RNN model for generating drum fills, conditioned by the previous bar. Then, we propose objective metrics to evaluate the quality of our generated drum fills, and the results of a user study we conducted. Please go to https://frederictamagnan.github.io/drumfills/ for details and audio examples.
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References
Buitinck, L., et al.: API design for machine learning software: experiences from the scikit-learn project. In: ECML PKDD Workshop: Languages for Data Mining and Machine Learning, pp 108–122 (2013)
Dong, H.W., Hsiao, W.Y., Yang, L.C., Yang, Y.H.: MuseGAN: multi-track sequential generative adversarial networks for symbolic music generation and accompaniment. In: Thirty-Second AAAI Conference on Artificial Intelligence (2018)
Dong, H. W., Yang, Y. H.: Convolutional generative adversarial networks with binary neurons for polyphonic music generation. In: ISMIR (2018)
Kingma, P., Ba, D., Adam, J.: A method for stochastic optimization. In: ICLR 2015 (2015)
Kingma, D.P., Welling, M.: Auto-encoding variational bayes. In: The International Conference on Learning Representations (2014)
Lehner, B., Widmer, G., Sonnleitner, R.: On the reduction of false positives in singing voice detection. In: ICASSP, pp. 7480–7484. IEEE (2014)
López-Serrano, P., Dittmar, C., Müller, M.: Finding drum breaks in digital music recordings. In: Aramaki, M., Davies, M.E.P., Kronland-Martinet, R., Ystad, S. (eds.) CMMR 2017. LNCS, vol. 11265, pp. 111–122. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-01692-0_8
Raffel, C.: Learning-based methods for comparing sequences, with applications to audio-to-midi alignment and matching. PhD Thesis (2016)
Roberts, A., Raffel, C., Engel, J., Hawthorne, C., Eck, D.: A hierarchical latent vector model for learning long-term structure in music. In: ICML 2018 (2018)
Play Drums Today. Hal Leonard, Milwaukee (2001). ISBN 0-634-02185-0
Acknowledgments
This work was done when FT was a visiting student at Academia Sinica.
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Tamagnan, F., Yang, YH. (2021). Drum Fills Detection and Generation. 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_6
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DOI: https://doi.org/10.1007/978-3-030-70210-6_6
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