Have a personal or library account? Click to login
Music Tempo Estimation: Are We Done Yet? Cover

References

  1. 1Allen, P. E., & Dannenberg, R. B. (1990). Tracking musical beats in real time. In Proceedings of the International Computer Music Conference, pages 140143. International Computer Music Association.
  2. 2Alonso, M., David, B., & Richard, G. (2003). A study of tempo tracking algorithms from polyphonic music signals. In 4th COST 276 Workshop.
  3. 3Bock, S., Davies, M. E., & Knees, P. (2019). Multitask learning of tempo and beat: Learning one to improve the other. In Proceedings of the International Society for Music Information Retrieval Conference (ISMIR), pages 486493. Delft, The Netherlands.
  4. 4Bock, S., Krebs, F., & Widmer, G. (2015). Accurate tempo estimation based on recurrent neural networks and resonating comb filters. In Proceedings of the 16th International Society for Music Information Retrieval Conference (ISMIR), pages 625631. Malaga, Spain.
  5. 5Bodoff, D. (2008). Test theory for evaluating reliability of IR test collections. Information Processing & Management, 44(3), 11171145. DOI: 10.1016/j.ipm.2007.11.006
  6. 6Bosch, J. J., Marxer, R., & Gomez, E. (2016). Evaluation and combination of pitch estimation methods for melody extraction in symphonic classical music. Journal of New Music Research, 45(2), 101117. DOI: 10.1080/09298215.2016.1182191
  7. 7Brennan, R. L. (2003). Generalizability theory. Journal of Educational Measurement, 40(1), 105107. DOI: 10.1111/j.1745-3984.2003.tb01098.x
  8. 8Carterette, B., Pavlu, V., Kanoulas, E., Aslam, J. A., & Allan, J. (2009). If I had a million queries. In European Conference on Information Retrieval, pages 288300. DOI: 10.1007/978-3-642-00958-7_27
  9. 9Cornelis, O., Six, J., Holzapfel, A., & Leman, M. (2013). Evaluation and recommendation of pulse and tempo annotation in ethnic music. Journal of New Music Research, 42(2), 131149. DOI: 10.1080/09298215.2013.812123
  10. 10Cronbach, L. J., Rajaratnam, N., & Gleser, G. C. (1963). Theory of generalizability: A liberalization of reliability theory. British Journal of Statistical Psychology, 16(2), 137163. DOI: 10.1111/j.2044-8317.1963.tb00206.x
  11. 11Davies, M. E., Plumbley, M. D., & Eck, D. (2009). Towards a musical beat emphasis function. In Proceedings of the IEEE Workshop on Applications of Signal Processing to Audio and Acoustics (WAS-PAA), pages 6164. New Paltz, NY, USA. IEEE. DOI: 10.1109/ASPAA.2009.5346462
  12. 12Dixon, S. (2001). Automatic extraction of tempo and beat from expressive performances. Journal of New Music Research, 30(1), 3958. DOI: 10.1076/jnmr.30.1.39.7119
  13. 13Downie, J. S. (2008). The music information retrieval evaluation exchange (2005–2007): A window into music information retrieval research. Acoustical Science and Technology, 29(4), 247255. DOI: 10.1250/ast.29.247
  14. 14Ellis, D. P., & Poliner, G. E. (2007). Identifying ‘cover songs’ with chroma features and dynamic pro-gramming beat tracking. In Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), volume 4, Honolulu, Hawaii, USA. DOI: 10.1109/ICASSP.2007.367348
  15. 15Elowsson, A. (2016). Beat tracking with a cepstroid invariant neural network. In Proceedings of the 17th International Society for Music Information Retrieval Conference (ISMIR), pages 351357. New York, NY, USA.
  16. 16Elowsson, A., & Friberg, A. (2015). Modeling the perception of tempo. Journal of the Acoustical Society of America, 137(6), 31633177. DOI: 10.1121/1.4919306
  17. 17Font, F., & Serra, X. (2016). Tempo estimation for music loops and a simple confidence measure. In Proceedings of the 17th International Society for Music Information Retrieval Conference (ISMIR), pages 269275. New York, NY, USA.
  18. 18Foroughmand, H., & Peeters, G. (2019). Deep-rhythm for tempo estimation and rhythm pattern recognition. In Proceedings of the International Society for Music Information Retrieval Conference (IS-MIR), pages 636643. Delft, The Netherlands.
  19. 19Friberg, A., & Sundberg, J. (1995). Time discrimination in a monotonic, isochronous sequence. Journal of the Acoustical Society of America, 98(5), 25242531. DOI: 10.1121/1.413218
  20. 20Gartner, D. (2013). Tempo detection of urban music using tatum grid non negative matrix factorization. In Proceedings of the 14th International Society for Music Information Retrieval Conference (ISMIR), pages 311316. Curitiba, Brazil.
  21. 21Gkiokas, A., Katsouros, V., Carayannis, G., & Stafylakis, T. (2012). Music tempo estimation and beat tracking by applying source separation and metrical relations. In Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), Kyoto, Japan. DOI: 10.1109/ICASSP.2012.6287906
  22. 22Goto, M., Hashiguchi, H., Nishimura, T., & Oka, R. (2002). RWC music database: Popular, classical and jazz music databases. In Proceedings of the International Conference on Music Information Retrieval (ISMIR). Paris, France.
  23. 23Goto, M., Hashiguchi, H., Nishimura, T., & Oka, R. (2003). RWC music database: Music genre database and musical instrument sound database. In Proceedings of the International Conference on Music Information Retrieval (ISMIR), pages 229230. Baltimore, MD, USA.
  24. 24Goto, M., & Muraoka, Y. (1994). A beat tracking system for acoustic signals of music. In Proceedings of the Second ACM International Conference on Multimedia, pages 365372. San Francisco, CA, USA. DOI: 10.1145/192593.192700
  25. 25Gouyon, F., Klapuri, A. P., Dixon, S., Alonso, M., Tzane- takis, G., Uhle, C., & Cano, P. (2006). An experimental comparison of audio tempo induction algorithms. IEEE Transactions on Audio, Speech, and Language Processing, 14(5), 18321844. DOI: 10.1109/TSA.2005.858509
  26. 26Hainsworth, S. W. (2004). Techniques for the Automated Analysis of Musical Audio. PhD thesis, University of Cambridge, UK.
  27. 27Holzapfel, A., Davies, M. E., Zapata, J. R., Oliveira, J. L., & Gouyon, F. (2012). Selective sampling for beat tracking evaluation. IEEE Transactions on Audio, Speech, and Language Processing, 20(9), 25392548. DOI: 10.1109/TASL.2012.2205244
  28. 28Humphrey, E. J., Salamon, J., Nieto, O., Forsyth, J., Bittner, R. M., & Bello, J. P. (2014). JAMS: A JSON annotated music specification for reproducible MIR research. In Proceedings of the 15th International Society for Music Information Retrieval Conference (ISMIR), pages 591596. Taipei, Taiwan.
  29. 29Klapuri, A. P., Eronen, A. J., & Astola, J. (2006). Analysis of the meter of acoustic musical signals. IEEE Transactions on Audio, Speech and Language Processing, 14(1), 342355. DOI: 10.1109/TSA.2005.854090
  30. 30Knees, P., Faraldo, A., Herrera, P., Vogl, R., Bock, S., Horschlager, F., & Le Goff, M. (2015). Two data sets for tempo estimation and key detection in electronic dance music annotated from user corrections. In Proceedings of the 16th International Society for Music Information Retrieval Conference (ISMIR), pages 364370. Malaga, Spain.
  31. 31Knees, P., & Schedl, M. (2016). Music Similarity and Retrieval: An Introduction to Audio- and Web-based Strategies. The Information Retrieval Series. Springer Berlin Heidelberg. DOI: 10.1007/978-3-662-49722-7_1
  32. 32Krebs, F., Bock, S., & Widmer, G. (2013). Rhythmic pattern modeling for beat and downbeat tracking in musical audio. In Proceedings of the 14th International Society for Music Information Retrieval Conference (ISMIR), pages 227232. Curitiba, Brazil.
  33. 33Lamere, P. (2009). In search of the click track. Blog post https://musicmachinery.com/2009/03/02/in-search-of-the-click-track/, last accessed 12/9/2018.
  34. 34Marchand, U., & Peeters, G. (2015). Swing Ratio Estimation. In Proceedings of the International Conference on Digital Audio Effects (DAFx). Trondheim, Norway.
  35. 35Marchand, U., & Peeters, G. (2016). The extended ballroom dataset. In Late Breaking Demo Session of the 17th International Society for Music Information Retrieval Conference (ISMIR). New York, NY, USA.
  36. 36McKinney, M. F., Moelants, D., Davies, M. E., & Klapuri, A. P. (2007). Evaluation of audio beat tracking and music tempo extraction algorithms. Journal of New Music Research, 36(1), 116. DOI: 10.1080/09298210701653252
  37. 37Moelants, D., & McKinney, M. F. (2004). 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, pages 558562.
  38. 38Oliveira, J. L., Gouyon, F., Martins, L. G., & Reis, L. P. (2010). IBT: A real-time tempo and beat tracking system. In Proceedings of the 11th International Society for Music Information Retrieval Conference (ISMIR), pages 291296.
  39. 39Peeters, G. (2005). Time variable tempo detection and beat marking. In Proceedings of the International Computer Music Conference (ICMC). Barcelona, Spain.
  40. 40Peeters, G. (2007). Template-based estimation of time- varying tempo. EURASIP Journal on Advances in Signal Processing, 2007(1), 158171. DOI: 10.1155/2007/67215
  41. 41Peeters, G., & Flocon-Cholet, J. (2012). Perceptual tempo estimation using GMM-regression. In Proceedings of the Second International ACM Workshop on Music Information Retrieval with User-Centered and Multimodal Strategies (MIRUM), pages 4550. New York, NY, USA. ACM. DOI: 10.1145/2390848.2390861
  42. 42Percival, G., & Tzanetakis, G. (2014). Streamlined tempo estimation based on autocorrelation and cross-correlation with pulses. IEEE/ACM Transactions on Audio, Speech and Language Processing (TASLP), 22(12), 17651776. DOI: 10.1109/TASLP.2014.2348916
  43. 43Raffel, C. (2016). Learning-Based Methods for Comparing Sequences, with Applications to Audio-to-MIDI Alignment and Matching. PhD thesis, Columbia University, USA. DOI: 10.1109/ICASSP.2016.7471641
  44. 44Raffel, C., McFee, B., Humphrey, E. J., Salamon, J., Nieto, O., Liang, D., & Ellis, D. P. W. (2014). mir_eval: A transparent implementation of common MIR metrics. In Proceedings of the 15th International Society for Music Information Retrieval Conference (ISMIR). Taipei, Taiwan.
  45. 45Salamon, J. (2019). What’s broken in music informatics research? Three uncomfortable statements. In 36th International Conference on Machine Learning (ICML), Workshop on Machine Learning for Music Discovery. Long Beach, CA, USA.
  46. 46Salamon, J., Gomez, E., Ellis, D. P., & Richard, G. (2014). Melody extraction from polyphonic music signals: Approaches, applications, and challenges. IEEE Signal Processing Magazine, 31(2), 118134. DOI: 10.1109/MSP.2013.2271648
  47. 47Salamon, J., & Urbano, J. (2012). Current challenges in the evaluation of predominant melody extraction algorithms. In Proceedings of the 13th International Society for Music Information Retrieval Conference (ISMIR), pages 289294. Porto, Portugal.
  48. 48Schedl, M., Flexer, A., & Urbano, J. (2013). The neglected user in music information retrieval research. Journal of Intelligent Information Systems, 41(3), 523539. DOI: 10.1007/s10844-013-0247-6
  49. 49Schedl, M., Zamani, H., Chen, C.-W., Deldjoo, Y., & Elahi, M. (2018). Current challenges and visions in music recommender systems research. International Journal of Multimedia Information Retrieval, 7(2), 95116. DOI: 10.1007/s13735-018-0154-2
  50. 50Scheirer, E. D. (1998). Tempo and beat analysis of acoustical musical signals. Journal of the Acoustical Society of America, 103(1), 588601. DOI: 10.1121/1.421129
  51. 51Schreiber, H., & Muller, M. (2014). Exploiting global features for tempo octave correction. In Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), pages 639643. Florence, Italy. DOI: 10.1109/ICASSP.2014.6853674
  52. 52Schreiber, H., & Muller, M. (2017). A post-processing procedure for improving music tempo estimates using supervised learning. In Proceedings of the 18th International Society for Music Information Retrieval Conference (ISMIR), pages 235242. Suzhou, China.
  53. 53Schreiber, H., & Muller, M. (2018a). A crowd-sourced experiment for tempo estimation of electronic dance music. In Proceedings of the 19th International Society for Music Information Retrieval Conference (ISMIR). Paris, France.
  54. 54Schreiber, H., & Muller, M. (2018b). A single-step approach to musical tempo estimation using a convolutional neural network. In Proceedings of the 19th International Society for Music Information Retrieval Conference (ISMIR). Paris, France.
  55. 55Serra, X. (2014). Creating research corpora for the computational study of music: The case of the CompMusic project. In Proceedings of the AES International Conference on Semantic Audio. London, UK. Audio Engineering Society.
  56. 56Serra, X., Magas, M., Benetos, E., Chudy, M., Dixon, S., Flexer, A., Gomez, E., Gouyon, F., Herrera, P., Jorda, S., Paytuvi, O., Peeters, G., Schluter, J., Vinet, H., & Widmer, G. (2013). Roadmap for Music Information ReSearch. http://mires.eecs.qmul.ac.uk/files/MIRES_Roadmap_ver_1.0.0.pdf
  57. 57Slaney, M. (2011). Web-scale multimedia analysis: Does content matter? IEEEMultiMedia, 18(2), 1215. DOI: 10.1109/MMUL.2011.34
  58. 58Sturm, B. L. (2013a). Classification accuracy is not enough. Journal of Intelligent Information Systems, 41(3), 371406. DOI: 10.1007/s10844-013-0250-y
  59. 59Sturm, B. L. (2013b). The GTZAN dataset: Its contents, its faults, their effects on evaluation, and its future use. CoRR, abs/1306.1461.
  60. 60Sturm, B. L. (2014). Faults in the ballroom dataset. Blog post http://media.aau.dk/null_space_pursuits/2014/01/ballroom-dataset.html, last accessed 4/29/2020.
  61. 61Sturm, B. L. (2016). Revisiting priorities: Improving MIR evaluation practices. In Proceedings of the 17th International Society for Music Information Retrieval Conference (ISMIR). New York, NY, USA.
  62. 62Sturm, B. L., Bardeli, R., Langlois, T., & Emiya, V. (2014). Formalizing the problem of music description. In Proceedings of the 15th International Society for Music Information Retrieval Conference (IS-MIR), pages 8994. Taipei, Taiwan.
  63. 63Tzanetakis, G., & Cook, P. (2002). Musical genre classification of audio signals. IEEE Transactions on Speech and Audio Processing, 10(5), 293302. DOI: 10.1109/TSA.2002.800560
  64. 64Tzanetakis, G., & Percival, G. (2013). An effective, simple tempo estimation method based on selfsimilarity and regularity. In Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP). Vancouver, Canada. DOI: 10.1109/ICASSP.2013.6637645
  65. 65Urbano, J., Schedl, M., & Serra, X. (2013). Evaluation in music information retrieval. Journal of Intelligent Information Systems, 41(3), 345369. DOI: 10.1007/s10844-013-0249-4
  66. 66Vignoli, F., & Pauws, S. (2005). A music retrieval system based on user driven similarity and its evaluation. In Proceedings of the 6th International Conference on Music Information Retrieval (ISMIR), pages 272279. London, UK.
  67. 67Waterhouse, J., Hudson, P., & Edwards, B. (2010). Effects of music tempo upon submaximal cycling performance. Scandinavian Journal of Medicine & Science in Sports, 20(4), 662669. DOI: 10.1111/j.1600-0838.2009.00948.x
  68. 68Zapata, J. R., & Gomez, E. (2011). Comparative evaluation and combination of audio tempo estimation approaches. In 42nd AES Conference on Semantic Audio. Ilmenau, Germany.
DOI: https://doi.org/10.5334/tismir.43 | Journal eISSN: 2514-3298
Language: English
Submitted on: Oct 16, 2019
Accepted on: Jul 7, 2020
Published on: Aug 24, 2020
Published by: Ubiquity Press
In partnership with: Paradigm Publishing Services
Publication frequency: 1 issue per year

© 2020 Hendrik Schreiber, Julián Urbano, Meinard Müller, published by Ubiquity Press
This work is licensed under the Creative Commons Attribution 4.0 License.