Abstract
This paper presents a novel method for the estimation of beat interval from audio files. As a first step, a feature extracted from the waveform is used to identify note onsets. The estimated note onsets are used as input to a beat induction algorithm, where the most probable beat interval is found. Several enhancements over existing beat estimation systems are proposed in this work, including methods for identifying the optimum audio feature and a novel weighting system in the beat induction algorithm. The resulting system works in real-time, and is shown to work well for a wide variety of contemporary and popular rhythmic music. Several real-time music control systems have been made using the presented beat estimation method.
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
Preview
Unable to display preview. Download preview PDF.
References
Goto, M., Muraoka, Y.: A real-time beat tracking system for audio signals. In: Proceedings of the International Computer Music Conference, pp. 171–174 (1995)
Goto, M., Muraoka, Y.: A real-time beat tracking for drumless audio signals: Chord change detection for musical decisions. Speech Communication 27, 311–335 (1998)
Scheirer, E.D.: Tempo and beat analysis of acoustic musical signals. J. Acoust. Soc. Am. 103, 588–601 (1998)
Dixon, S.: Automatic extraction of tempo and beat from expressive performances. Journal of New Music Research 30, 39–58 (2001)
Desain, P.: A (de)composable theory of rhythm. Music Perception 9, 439–454 (1992)
Andersen, T.H.: Mixxx: Towards novel DJ interfaces. In: Conference on New Interfaces for Musical Expression (NIME 2003), Montreal (2003)
Murphy, D., Andersen, T.H., Jensen, K.: Conducting audio files via computer vision. In: Proceedings of the Gesture Workshop, Genova (2003)
McAdams, S., Winsberg, S., Donnadieu, S., Soete, G.D., Krimphoff, J.: Perceptual scaling of synthesized musical timbres: Common dimensions, specificities, and latent subject classes. Psychological Research 58, 177–192 (1995)
Jensen, K., Murphy, D.: Segmenting melodies into notes. In: Proceedings of the DSAGM, Copenhagen, Denmark (2001)
Dixon, S., Goebl, W., Widmer, G.: Real time tracking and visualisation of musical expression. In: II International Conference on Music and Artificial Intelligence, Edinburgh, Scotland, vol. 12, pp. 58–68 (2002)
Beauchamp, J.: Synthesis by spectral amplitude and “brightness” matching of analyzed musical instrument tones. Journal of the Acoustical Society of America 30 (1982)
Masri, P., Bateman, A.: Improved modelling of attack transient in music analysisresynthesis. In: Proceedings of the International Computer Music Conference, Hong-Kong, pp. 100–104 (1996)
Gordon, J.W.: The perceptual attack time of musical tones. J. Acoust. Soc. Am. 82 (1987)
Andersen, T.H., Andersen, K.H.: Mixxx (2003), http://mixxx.sourceforge.net/
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2004 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Jensen, K., Andersen, T.H. (2004). Real-Time Beat EstimationUsing Feature Extraction. In: Wiil, U.K. (eds) Computer Music Modeling and Retrieval. CMMR 2003. Lecture Notes in Computer Science, vol 2771. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-39900-1_2
Download citation
DOI: https://doi.org/10.1007/978-3-540-39900-1_2
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-20922-5
Online ISBN: 978-3-540-39900-1
eBook Packages: Springer Book Archive