Skip to main content

Tempo and Beat Tracking

  • Chapter
  • First Online:
Fundamentals of Music Processing

Abstract

Temporal and structural regularities are perhaps the most important incentives for people to get involved and to interact with music. It is the beat that drives music forward and provides the temporal framework of a piece of music. Intuitively, the beat corresponds to the pulse a human taps along when listening to music.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 54.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 69.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. J. P. Bello, L. Daudet, S. Abdallah, C. Duxbury, M. Davies, AND M. Sandler, A tutorial on onset detection in music signals, IEEE Transactions on Speech and Audio Processing, 13 (2005), pp. 1035–1047.

    Google Scholar 

  2. J. A. Bilmes, Techniques to foster drum machine expressivity, in International Computer Music Conference, Tokyo, Japan, 1993.

    Google Scholar 

  3. A. T. Cemgil, B. Kappen, P. Desain, and H. Honing, On tempo tracking: Tempogram representation and Kalman filtering, Journal of New Music Research, 28 (2001), pp. 259–273.

    Google Scholar 

  4. N. Collins, A comparison of sound onset detection algorithms with emphasis on psychoacoustically motivated detection functions, in AES Convention 118, Barcelona, Spain, 2005.

    Google Scholar 

  5. ———, Using a pitch detector for onset detection, in Proceedings of the International Society for Music Information Retrieval Conference (ISMIR), London, UK, 2005, pp. 100–106.

    Google Scholar 

  6. R. B. Dannenberg, Toward automated holistic beat tracking, music analysis and understanding, in Proceedings of the International Society for Music Information Retrieval Conference (ISMIR), London, UK, 2005, pp. 366–373.

    Google Scholar 

  7. M. E. Davies, N. Degara, and M. D. Plumbley, Measuring the performance of beat tracking algorithms using a beat error histogram, IEEE Signal Processing Letters, 18 (2011), pp. 157–160.

    Google Scholar 

  8. M. E. Davies and M. D. Plumbley, Context-dependent beat tracking of musical audio, IEEE Transactions on Audio, Speech, and Language Processing, 15 (2007), pp. 1009–1020.

    Google Scholar 

  9. M. E. P. Davies and S. Böck, Evaluating the evaluation measures for beat tracking, in Proceedings of the International Society for Music Information Retrieval Conference (ISMIR), Taipei, Taiwan, 2014, pp. 637–642.

    Google Scholar 

  10. N. Degara, M. E. Davies, A. Pena, and M. D. Plumbley, Onset event decoding exploiting the rhythmic structure of polyphonic music, IEEE Journal of Selected Topics in Signal Processing, 5 (2011), pp. 1228–1239.

    Google Scholar 

  11. N. Degara, A. Pena, M. E. Davies, and M. D. Plumbley, Note onset detection using rhythmic structure, in Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), Dallas, Texas, USA, 2010, pp. 5526–5529.

    Google Scholar 

  12. N. Degara, A. Pena, and S. Torres-Guijarro, A comparison of score-level fusion rules for onset detection in music signals, in Proceedings of the International Society for Music Information Retrieval Conference (ISMIR), Kobe, Japan, 2009, pp. 117–122.

    Google Scholar 

  13. N. Degara, E. A. Rúa, A. Pena, S. Torres-Guijarro, M. E. P. Davies, and M. D. Plumbley, Reliability-informed beat tracking of musical signals, IEEE Transactions on Audio, Speech, and Language Processing, 20 (2012), pp. 290–301.

    Google Scholar 

  14. S. Dixon, Automatic extraction of tempo and beat from expressive performances, Journal of New Music Research, 30 (2001), pp. 39–58.

    Google Scholar 

  15. ———, An empirical comparison of tempo trackers, in Proceedings of the Brazilian Symposium on Computer Music (SBCM), Fortaleza, Brazil, 2001, pp. 832–840.

    Google Scholar 

  16. ———, Onset detection revisited, in Proceedings of the International Conference on Digital Audio Effects (DAFx), Montreal, Quebec, Canada, 2006, pp. 133–137.

    Google Scholar 

  17. ———, Evaluation of the audio beat tracking system BeatRoot, Journal of New Music Research, 36 (2007), pp. 39–50.

    Google Scholar 

  18. S. Dixon, W. Goebl, and E. Cambouropoulos, Perceptual smoothness of tempo in expressively performed music, Music Perception, 23 (2006), pp. 195–214.

    Google Scholar 

  19. S. Dixon, F. Gouyon, and G. Widmer, Towards characterisation of music via rhythmic patterns, in Proceedings of the International Society for Music Information Retrieval Conference (ISMIR), Barcelona, Spain, 2004.

    Google Scholar 

  20. J. S. Downie, The music information retrieval evaluation exchange (2005–2007): A window into music information retrieval research, Acoustical Science and Technology, 29 (2008), pp. 247–255.

    Google Scholar 

  21. D. P. Ellis, Beat tracking by dynamic programming, Journal of New Music Research, 36 (2007), pp. 51–60. 348 6 Tempo and Beat Tracking

    Google Scholar 

  22. D. P. Ellis, C. V. Cotton, and M. I. Mandel, Cross-correlation of beat-synchronous representations for music similarity, in Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), Taipei, Taiwan, 2008, pp. 57–60.

    Google Scholar 

  23. A. J. Eronen and A. P. Klapuri, Music tempo estimation with k-NN regression, IEEE Transactions on Audio, Speech, and Language Processing, 18 (2010), pp. 50–57.

    Google Scholar 

  24. F. Eyben, S. Böck, B. Schuller, and A. Graves, Universal onset detection with bidirectional long short-term memory neural networks, in Proceedings of the International Society for Music Information Retrieval Conference ISMIR, Utrecht, The Netherlands, 2010, pp. 589–594.

    Google Scholar 

  25. J. Foote and S. Uchihashi, The beat spectrum: A new approach to rhythm analysis, in Proceedings of the International Conference on Multimedia and Expo (ICME), Los Alamitos, California, USA, 2001.

    Google Scholar 

  26. M. Goto, An audio-based real-time beat tracking system for music with or without drumsounds, Journal of New Music Research, 30 (2001), pp. 159–171.

    Google Scholar 

  27. F. Gouyon and S. Dixon, A review of automatic rhythm description systems, Computer Music Journal, 29 (2005), pp. 34–54.

    Google Scholar 

  28. F. Gouyon, S. Dixon, and G. Widmer, Evaluating low-level features for beat classification and tracking, in Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), Honolulu, Hawaii, USA, 2007.

    Google Scholar 

  29. F. Gouyon and P. Herrera, Pulse-dependent analysis of percussive music, in Proceedings of the AES International Conference on Virtual, Synthetic and Entertainment Audio, Espoo, Finland, 2002.

    Google Scholar 

  30. F. Gouyon, A. P. Klapuri, S. Dixon, M. Alonso, G. Tzanetakis, C. Uhle, and P. Cano, An experimental comparison of audio tempo induction algorithms, IEEE Transactions on Audio, Speech, and Language Processing, 14 (2006), pp. 1832–1844.

    Google Scholar 

  31. P. Grosche, Signal Processing Methods for Beat Tracking, Music Segmentation, and Audio Retrieval, PhD thesis, Saarland University and MPI Informatik, 2012.

    Google Scholar 

  32. P. Grosche and M. Müller, A mid-level representation for capturing dominant tempo and pulse information in music recordings, in Proceedings of the International Society for Music Information Retrieval Conference (ISMIR), Kobe, Japan, 2009, pp. 189–194.

    Google Scholar 

  33. ———, Extracting predominant local pulse information from music recordings, IEEE Transactions on Audio, Speech, and Language Processing, 19 (2011), pp. 1688–1701.

    Google Scholar 

  34. ———, Tempogram toolbox: Matlab implementations for tempo and pulse analysis of music recordings, in Late-Breaking News of the International Society for Music Information Retrieval Conference (ISMIR), Miami, Florida, USA, 2011.

    Google Scholar 

  35. P. Grosche, M. Müller, and F. Kurth, Cyclic tempogram – a mid-level tempo representation for music signals, in Proceedings of IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), Dallas, Texas, USA, 2010, pp. 5522–5525.

    Google Scholar 

  36. P. Grosche, M. Müller, and C. S. Sapp, What makes beat tracking difficult? A case study on Chopin Mazurkas, in Proceedings of the International Society for Music Information Retrieval Conference (ISMIR), Utrecht, The Netherlands, 2010, pp. 649–654.

    Google Scholar 

  37. J. Hockman, M. E. P. Davies, and I. Fujinaga, One in the jungle: Downbeat detection in hardcore, jungle, and drum and bass, in Proceedings of the International Society for Music Information Retrieval Conference (ISMIR), Porto, Portugal, 2012, pp. 169–174.

    Google Scholar 

  38. A. Holzapfel, M. E. P. Davies, J. R. Zapata, J. L. Oliveira, and F. Gouyon, Selective sampling for beat tracking evaluation, IEEE Transactions on Audio, Speech, and Language Processing, 20 (2012), pp. 2539–2548.

    Google Scholar 

  39. A. Holzapfel and Y. Stylianou, Beat tracking using group delay based onset detection, in Proceedings of the International Society for Music Information Retrieval Conference (ISMIR), Philadelphia, USA, 2008.

    Google Scholar 

  40. A. Holzapfel, Y. Stylianou, A. C. Gedik, and B. Bozkurt, Three dimensions of pitched instrument onset detection, IEEE Transactions on Audio, Speech, and Language Processing, 18 (2010), pp. 1517–1527.

    Google Scholar 

  41. A. Holzapfel, G. A. Velasco, N. Holighaus, M. Dörfler, and A. Flexer, Advantages of nonstationary Gabor transforms in beat tracking, in Proceedings of the International ACM Workshop on Music information retrieval with user-centered and multimodal strategies (MIRUM), Scottsdale, Arizona, USA, 2011, pp. 45–50.

    Google Scholar 

  42. K. Jensen, J. Xu, and M. Zachariasen, Rhythm-based segmentation of popular Chinese music, in Proceedings of the International Society for Music Information Retrieval Conference (ISMIR), London, UK, 2005.

    Google Scholar 

  43. A. P. Klapuri, Sound onset detection by applying psychoacoustic knowledge, in Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), Washington, DC, USA, 1999, pp. 3089–3092.

    Google Scholar 

  44. A. P. Klapuri, A. J. Eronen, and J. Astola, Analysis of the meter of acoustic musical signals, IEEE Transactions on Audio, Speech, and Language Processing, 14 (2006), pp. 342–355.

    Google Scholar 

  45. V. Konz, M. Müller, and S. Ewert, A multi-perspective evaluation framework for chord recognition, in Proceedings of the International Society for Music Information Retrieval Conference (ISMIR), Utrecht, The Netherlands, 2010, pp. 9–14.

    Google Scholar 

  46. F. Kurth, T. Gehrmann, and M. Müller, The cyclic beat spectrum: Tempo-related audio features for time-scale invariant audio identification, in Proceedings of the International Conference on Music Information Retrieval (ISMIR), Victoria, Canada, 2006, pp. 35–40.

    Google Scholar 

  47. A. Lacoste and D. Eck, A supervised classification algorithm for note onset detection, EURASIP Journal on Applied Signal Processing, (2007), pp. 153–165.

    Google Scholar 

  48. E. W. Large and C. Palmer, Perceiving temporal regularity in music, Cognitive Science, 26 (2002), pp. 1–37.

    Google Scholar 

  49. F. Lerdahl and R. Jackendoff, A Generative Theory of Tonal Music, MIT Press, 1983.

    Google Scholar 

  50. P. Masri and A. Bateman, Improved modeling of attack transients in music analysis resynthesis, in Proceedings of the International Computer Music Conference (ICMC), Hong Kong, 1996, pp. 100–103.

    Google Scholar 

  51. M. F. Mckinney, D. Moelants, M. E. Davies, and A. P. Klapuri, Evaluation of audio beat tracking and music tempo extraction algorithms, Journal of New Music Research, 36 (2007), pp. 1–16.

    Google Scholar 

  52. B. C. J. Moore, An introduction to the psychology of hearing, 5th ed., Academic Press, 2003.

    Google Scholar 

  53. H. Papadopoulos and G. Peeters, Simultaneous estimation of chord progression and downbeats from an audio file, in Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), 2008, pp. 121–124.

    Google Scholar 

  54. ———, Joint estimation of chords and downbeats from an audio signal, IEEE Transactions on Audio, Speech, and Language Processing, 19 (2011), pp. 138–152.

    Google Scholar 

  55. R. Parncutt, A perceptual model of pulse salience and metrical accent in musical rhythms, Music Perception, 11 (1994), pp. 409–464.

    Google Scholar 

  56. J. Paulus and A. P. Klapuri, Measuring the similarity of rhythmic patterns, in Proceedings of the International Society for Music Information Retrieval Conference (ISMIR), Paris, France, 2002, pp. 150–156.

    Google Scholar 

  57. J. Paulus, M. Müller, and A. P. Klapuri, Audio-based music structure analysis, in Proceedings of the International Society for Music Information Retrieval Conference (ISMIR), Utrecht, The Netherlands, 2010, pp. 625–636.

    Google Scholar 

  58. G. Peeters, Rhythm classification using spectral rhythm patterns, in Proceedings of the International Society for Music Information Retrieval Conference (ISMIR), London, UK, 2005, pp. 644–647.

    Google Scholar 

  59. ———, Time variable tempo detection and beat marking, in Proceedings of the International Computer Music Conference (ICMC), Barcelona, Spain, 2005.

    Google Scholar 

  60. G. Peeters, Template-based estimation of time-varying tempo, EURASIP Journal on Advances in Signal Processing, (2007).

    Google Scholar 

  61. ———, “Copy and scale” method for doing time-localized M.I.R. estimation: application to beat-tracking, in Proceedings of 3rd international workshop on Machine learning and music (MML), Firenze, Italy, 2010, pp. 1–4.

    Google Scholar 

  62. G. Peeters and H. Papadopoulos, Simultaneous beat and downbeat-tracking using a probabilistic framework: Theory and large-scale evaluation, IEEE Transactions on Audio, Speech, and Language Processing, 19 (2011), pp. 1754–1769.

    Google Scholar 

  63. S.-C. Pei and N.-T. Hsu, Instrumentation analysis and identification of polyphonic music using beat-synchronous feature integration and fuzzy clustering, in Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), Taipei, Taiwan, 2009, pp. 169–172.

    Google Scholar 

  64. C. S. Sapp, Comparative analysis of multiple musical performances, in Proceedings of the International Society for Music Information Retrieval Conference (ISMIR), Vienna, Austria, 2007, pp. 497–500.

    Google Scholar 

  65. E. D. Scheirer, Tempo and beat analysis of acoustical musical signals, Journal of the Acoustical Society of America, 103 (1998), pp. 588–601.

    Google Scholar 

  66. B. Schuller, F. Eyben, and G. Rigoll, Fast and robust meter and tempo recognition for the automatic discrimination of ballroom dance styles, in Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), 2007, pp. 217–220.

    Google Scholar 

  67. J. Seppänen, Tatum grid analysis of musical signals, in Proceedings of the IEEE Workshop on Applications of Signal Processing to Audio and Acoustics (WASPAA), 2001, pp. 131–134.

    Google Scholar 

  68. J. Seppänen, A. J. Eronen, and J. Hiipakka, Joint beat & tatum tracking from music signals, in Proceedings of the International Society for Music Information Retrieval Conference (ISMIR), Victoria, Canada, 2006.

    Google Scholar 

  69. W. A. Sethares, Rhythm and Transforms, Springer, 2007.

    Google Scholar 

  70. D. Stowell and M. Plumbley, Adaptive whitening for improved real-time audio onset detection, in Proceedings of the International Computer Music Conference (ICMC), Copenhagen, Denmark, 2007.

    Google Scholar 

  71. C. C. Toh, B. Zhang, and Y. Wang, Multiple-feature fusion based onset detection for solo singing voice, in Proceedings of the International Society for Music Information Retrieval Conference (ISMIR), Philadelphia, PA, USA, 2008, pp. 515–520.

    Google Scholar 

  72. G. Tzanetakis and G. Percival, An effective, simple tempo estimation method based on self-similarity and regularity, in IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), Vancouver, Canada, 2013, pp. 241–245.

    Google Scholar 

  73. F.-H. F. Wu, T.-C. Lee, J.-S. R. Jang, K. K. Chang, C.-H. Lu, and W.-N. Wang, A two-fold dynamic programming approach to beat tracking for audio music with time-varying tempo, in Proceedings of the International Society for Music Information Retrieval Conference (ISMIR), Miami, Florida, USA, 2011, pp. 191–196.

    Google Scholar 

  74. J. R. Zapata, M. E. P. Davies, and E. Gómez, Multi-feature beat tracking, IEEE/ACM Transactions on Audio, Speech, and Language Processing, 22 (2014), pp. 816–825.

    Google Scholar 

  75. J. R. Zapata and E. Gómez, Comparative evaluation and combination of audio tempo estimation approaches, in Proceedings of the AES Conference on Semantic Audio, Ilmenau, Germany, 2011.

    Google Scholar 

  76. R. Zhou, M. Mattavelli, and G. Zoia, Music onset detection based on resonator time frequency image, IEEE Transactions on Audio, Speech, and Language Processing, 16 (2008), pp. 1685–1695.

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Meinard Müller .

Rights and permissions

Reprints and permissions

Copyright information

© 2015 Springer International Publishing Switzerland

About this chapter

Cite this chapter

Müller, M. (2015). Tempo and Beat Tracking. In: Fundamentals of Music Processing. Springer, Cham. https://doi.org/10.1007/978-3-319-21945-5_6

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-21945-5_6

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-21944-8

  • Online ISBN: 978-3-319-21945-5

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics