References
- 1Allen, P. E., & Dannenberg, R. B. (1990). Tracking musical beats in real time. In Proceedings of the International Computer Music Conference, pages 140–143.
International Computer Music Association . - 2Alonso, M., David, B., & Richard, G. (2003). A study of tempo tracking algorithms from polyphonic music signals. In 4th COST 276 Workshop.
- 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 486–493. Delft, The Netherlands.
- 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 625–631. Malaga, Spain.
- 5Bodoff, D. (2008). Test theory for evaluating reliability of IR test collections. Information Processing & Management, 44(3), 1117–1145. DOI: 10.1016/j.ipm.2007.11.006
- 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), 101–117. DOI: 10.1080/09298215.2016.1182191
- 7Brennan, R. L. (2003). Generalizability theory. Journal of Educational Measurement, 40(1), 105–107. DOI: 10.1111/j.1745-3984.2003.tb01098.x
- 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 288–300. DOI: 10.1007/978-3-642-00958-7_27
- 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), 131–149. DOI: 10.1080/09298215.2013.812123
- 10Cronbach, L. J., Rajaratnam, N., & Gleser, G. C. (1963). Theory of generalizability: A liberalization of reliability theory. British Journal of Statistical Psychology, 16(2), 137–163. DOI: 10.1111/j.2044-8317.1963.tb00206.x
- 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 61–64. New Paltz, NY, USA.
IEEE . DOI: 10.1109/ASPAA.2009.5346462 - 12Dixon, S. (2001). Automatic extraction of tempo and beat from expressive performances. Journal of New Music Research, 30(1), 39–58. DOI: 10.1076/jnmr.30.1.39.7119
- 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), 247–255. DOI: 10.1250/ast.29.247
- 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
- 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 351–357. New York, NY, USA.
- 16Elowsson, A., & Friberg, A. (2015). Modeling the perception of tempo. Journal of the Acoustical Society of America, 137(6), 3163–3177. DOI: 10.1121/1.4919306
- 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 269–275. New York, NY, USA.
- 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 636–643. Delft, The Netherlands.
- 19Friberg, A., & Sundberg, J. (1995). Time discrimination in a monotonic, isochronous sequence. Journal of the Acoustical Society of America, 98(5), 2524–2531. DOI: 10.1121/1.413218
- 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 311–316. Curitiba, Brazil.
- 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
- 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.
- 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 229–230. Baltimore, MD, USA.
- 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 365–372. San Francisco, CA, USA. DOI: 10.1145/192593.192700
- 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), 1832–1844. DOI: 10.1109/TSA.2005.858509
- 26Hainsworth, S. W. (2004). Techniques for the Automated Analysis of Musical Audio. PhD thesis, University of Cambridge, UK.
- 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), 2539–2548. DOI: 10.1109/TASL.2012.2205244
- 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 591–596. Taipei, Taiwan.
- 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), 342–355. DOI: 10.1109/TSA.2005.854090
- 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 364–370. Malaga, Spain.
- 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
- 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 227–232. Curitiba, Brazil.
- 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. - 34Marchand, U., & Peeters, G. (2015). Swing Ratio Estimation. In Proceedings of the International Conference on Digital Audio Effects (DAFx). Trondheim, Norway.
- 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.
- 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), 1–16. DOI: 10.1080/09298210701653252
- 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 558–562.
- 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 291–296.
- 39Peeters, G. (2005). Time variable tempo detection and beat marking. In Proceedings of the International Computer Music Conference (ICMC). Barcelona, Spain.
- 40Peeters, G. (2007). Template-based estimation of time- varying tempo. EURASIP Journal on Advances in Signal Processing, 2007(1), 158–171. DOI: 10.1155/2007/67215
- 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 45–50. New York, NY, USA.
ACM . DOI: 10.1145/2390848.2390861 - 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), 1765–1776. DOI: 10.1109/TASLP.2014.2348916
- 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
- 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.
- 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.
- 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), 118–134. DOI: 10.1109/MSP.2013.2271648
- 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 289–294. Porto, Portugal.
- 48Schedl, M., Flexer, A., & Urbano, J. (2013). The neglected user in music information retrieval research. Journal of Intelligent Information Systems, 41(3), 523–539. DOI: 10.1007/s10844-013-0247-6
- 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), 95–116. DOI: 10.1007/s13735-018-0154-2
- 50Scheirer, E. D. (1998). Tempo and beat analysis of acoustical musical signals. Journal of the Acoustical Society of America, 103(1), 588–601. DOI: 10.1121/1.421129
- 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 639–643. Florence, Italy. DOI: 10.1109/ICASSP.2014.6853674
- 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 235–242. Suzhou, China.
- 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.
- 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.
- 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 . - 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 - 57Slaney, M. (2011). Web-scale multimedia analysis: Does content matter? IEEEMultiMedia, 18(2), 12–15. DOI: 10.1109/MMUL.2011.34
- 58Sturm, B. L. (2013a). Classification accuracy is not enough. Journal of Intelligent Information Systems, 41(3), 371–406. DOI: 10.1007/s10844-013-0250-y
- 59Sturm, B. L. (2013b). The GTZAN dataset: Its contents, its faults, their effects on evaluation, and its future use. CoRR, abs/1306.1461.
- 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. - 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.
- 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 89–94. Taipei, Taiwan.
- 63Tzanetakis, G., & Cook, P. (2002). Musical genre classification of audio signals. IEEE Transactions on Speech and Audio Processing, 10(5), 293–302. DOI: 10.1109/TSA.2002.800560
- 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
- 65Urbano, J., Schedl, M., & Serra, X. (2013). Evaluation in music information retrieval. Journal of Intelligent Information Systems, 41(3), 345–369. DOI: 10.1007/s10844-013-0249-4
- 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 272–279. London, UK.
- 67Waterhouse, J., Hudson, P., & Edwards, B. (2010). Effects of music tempo upon submaximal cycling performance. Scandinavian Journal of Medicine & Science in Sports, 20(4), 662–669. DOI: 10.1111/j.1600-0838.2009.00948.x
- 68Zapata, J. R., & Gomez, E. (2011). Comparative evaluation and combination of audio tempo estimation approaches. In 42nd AES Conference on Semantic Audio. Ilmenau, Germany.
