References
- 1Burgoyne, J. A., Pugin, L., Kereliuk, C., and Fujinaga, I. (2007). A cross-validated study of modelling strategies for automatic chord recognition in audio. In Proceedings of the 8th International Conference on Music Information Retrieval, pages 251–254.
- 2Cannam, C., Benetos, E., Davies, M. E., Dixon, S., Landone, C., Levy, M., Mauch, M., Noland, K., and Stowell, D. (2018). MIREX 2018: VAMP plugins from the Centre for Digital Music. Proceedings of the Music Information Retrieval Evaluation eXchange.
- 3Chen, T.-P. and Su, L. (2019). Harmony Transformer: Incorporating chord segmentation into harmony recognition. In Proceedings of the 20th International Society for Music Information Retrieval Conference, pages 259–267.
- 4Cuvillier, P. and Cont, A. (2014). Coherent time modeling of semi-Markov models with application to real-time audio-to-score alignment. In IEEE International Workshop on Machine Learning for Signal Processing (MLSP), pages 1–6.
IEEE . DOI: 10.1109/MLSP.2014.6958908 - 5Ewert, S., Müller, M., Konz, V., Müllensiefen, D., and Wiggins, G. A. (2012). Towards cross-version harmonic analysis of music. IEEE Transactions on Multimedia, 14(3):770–782. DOI: 10.1109/TMM.2012.2190047
- 6Friedman, M. (1937). The use of ranks to avoid the assumption of normality implicit in the analysis of variance. Journal of the American Statistical Association, 32(200):675–701. DOI: 10.1080/01621459.1937.10503522
- 7Gasser, S. and Strasser, F. (2018). Multi objective chord estimation. Proceedings of the Music Information Retrieval Evaluation eXchange.
- 8Harte, C. (2010). Towards Automatic Extraction of Harmony Information from Music Signals. PhD thesis, Department of Electronic Engineering, Queen Mary University of London.
- 9Humphrey, E. J. and Bello, J. P. (2015). Four timely insights on automatic chord estimation. In Proceedings of the 16th International Society for Music Information Retrieval Conference, pages 673–679.
- 10Jiang, J., Chen, K., Li, W., and Xia, G. (2018). MIREX 2018 submission: A structural chord representation for automatic large-vocabulary chord transcription. Proceedings of the Music Information Retrieval Evaluation eXchange.
- 11Jiang, J., Li, W., and Wu, Y. (2017). Extended abstract for MIREX 2017 submission: Chord recognition using random forest model. Proceedings of the Music Information Retrieval Evaluation eXchange.
- 12Konz, V. and Müller, M. (2012).
A cross-version approach for harmonic analysis of music recordings . In Müller, M., Goto, M., and Schedl, M., editors, Multimodal Music Processing, volume 3 of Dagstuhl Follow-Ups, pages 53–72. Schloss Dagstuhl–Leibniz-Zentrum für Informatik, Dagstuhl, Germany. - 13Koops, H. V., de Haas, W. B., Bountouridis, D., and Volk, A. (2016). Integration and quality assessment of heterogeneous chord sequences using data fusion. In Proceedings of the 17th International Society for Music Information Retrieval Conference, pages 178–184.
- 14Koops, H. V., de Haas, W. B., Bransen, J., and Volk, A. (2017). Chord label personalization through deep learning of integrated harmonic intervalbased representations. In Proceedings of the First International Workshop on Deep Learning for Music, pages 19–25.
- 15Koops, H. V., de Haas, W. B., Burgoyne, J. A., Bransen, J., Kent-Muller, A., and Volk, A. (2019). Annotator subjectivity in harmony annotations of popular music. Journal of New Music Research, 48(3):232–252. DOI: 10.1080/09298215.2019.1613436
- 16Korzeniowski, F. and Widmer, G. (2016a). Feature learning for chord recognition: The Deep Chroma Extractor. In Proceedings of 17th International Conference on Music Information Retrieval.
- 17Korzeniowski, F. and Widmer, G. (2016b). A fully convolutional deep auditory model for musical chord recognition. In Proceedings of the 26th International Workshop on Machine Learning for Signal Processing, pages 1–6.
IEEE . DOI: 10.1109/MLSP.2016.7738895 - 18Lee, S.-R., Chien, I., Yeh, T.-C., and Jang, J.-S. R. (2019). MIREX 2019 submission: Chord estimation. Proceedings of the Music Information Retrieval Evaluation eXchange.
- 19Li, X., Dong, X. L., Lyons, K., Meng, W., and Srivastava, D. (2012). Truth finding on the deep web: Is the problem solved? Proceedings of the VLDB Endowment, 6(2). DOI: 10.14778/2535568.2448943
- 20Macrae, R. (2012). Linking Music Metadata. PhD thesis, Queen Mary University of London.
- 21Masada, K. and Bunescu, R. (2019). Chord recognition in symbolic music: A segmental CRF model, segment-level features, and comparative evaluations on classical and popular music. Transactions of the International Society for Music Information Retrieval, 2(1). DOI: 10.5334/tismir.18
- 22Mauch, M. (2010). Automatic Chord Transcription from Audio Using Computational Models of Musical Context. PhD thesis, Queen Mary University of London.
- 23Mauch, M., Cannam, C., Davies, M., Dixon, S., Harte, C., Kolozali, S., Tidhar, D., and Sandler, M. (2009). OMRAS2 metadata project 2009. In Late-breaking Session at the 10th International Conference on Music Information Retrieval.
- 24Maxwell, H. J. (1992). An expert system for harmonic analysis of tonal music. In Proceedings of the First Workshop on AI and Music, pages 20–33.
AAAI . - 25McFee, B., McVicar, M., Balke, S., Thomé, C., Raffel, C., Lee, D., Nieto, O., Battenberg, E., Ellis, D., Yamamoto, R., Moore, J., Bittner, R., Choi, K., Friesch, P., Stöter, F.-R., Lostanlen, V., Kumar, S., Waloschek, S., Seth, Naktinis, R., Repetto, D., Hawthorne, C. F., Carr, C., Pimenta, W., Viktorin, P., Brossier, P., Santos, J. F., JackieWu, Erik, and Holovaty, A. (2018). librosa/librosa: 0.6.1.
- 26McVicar, M., Ni, Y., Santos-Rodriguez, R., and De Bie, T. (2011). Using online chord databases to enhance chord recognition. Journal of New Music Research, 40(2):139–152. DOI: 10.1080/09298215.2011.573564
- 27McVicar, M., Santos-Rodríguez, R., Ni, Y., and De Bie, T. (2014). Automatic chord estimation from audio: A review of the state of the art. IEEE/ACM Transactions on Audio, Speech and Language Processing, 22(2):556–575. DOI: 10.1109/TASLP.2013.2294580
- 28Ni, Y., McVicar, M., Santos-Rodriguez, R., and De Bie, T. (2013). Understanding effects of subjectivity in measuring chord estimation accuracy. IEEE Transactions on Audio, Speech, and Language Processing, 21(12):2607–2615. DOI: 10.1109/TASL.2013.2280218
- 29Pardo, B. and Birmingham, W. P. (2002). Algorithms for chordal analysis. Computer Music Journal, 26(2):27–49. DOI: 10.1162/014892602760137167
- 30Pauwels, J., O’Hanlon, K., Gómez, E., and Sandler, M. (2019). 20 years of automatic chord recognition from audio. In Proceedings of the 20th International Society for Music Information Retrieval Conference, pages 54–63.
- 31Preston, J. B., Pietras, M., and Robinson, L. (2012). Three views of the “musical work”: Bibliographical control in the music domain. Library Review. DOI: 10.1108/00242531211292060
- 32Radicioni, D. and Esposito, R. (2010). BREVE: An HMPerceptron-based chord recognition system. Advances in Music Information Retrieval, pages 143–164. DOI: 10.1007/978-3-642-11674-2_7
- 33Raffel, C. and Ellis, D. P. (2014). Intuitive analysis, creation and manipulation of MIDI data with pretty_midi. In 15th International Society for Music Information Retrieval Conference Late Breaking and Demo Papers, pages 84–93.
- 34Raffel, C. and Ellis, D. P. (2015). Large-scale contentbased matching of MIDI and audio files. In Proceedings of the 16th International Society for Music Information Retrieval Conference, pages 234–240.
- 35Raffel, C. and Ellis, D. P. (2016). Optimizing DTWbased audio-to-MIDI alignment and matching. In IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pages 81–85.
IEEE . DOI: 10.1109/ICASSP.2016.7471641 - 36Raphael, C. and Stoddard, J. (2004). Functional harmonic analysis using probabilistic models. Computer Music Journal, 28(3):45–52. DOI: 10.1162/0148926041790676
- 37Scholz, R. and Ramalho, G. (2008). COCHONUT: Recognizing complex chords from MIDI guitar sequences. In Proceedings of the 9th International Conference on Music Information Retrieval, pages 27–32.
- 38Scholz, R., Ramalho, G., and Cabral, G. (2016). Cross task study on MIREX recent results: An index for evolution measurement and some stagnation hypotheses. In Proceedings of the 17th International Society for Music Information Retrieval Conference, pages 372–378.
- 39Sheh, A. and Ellis, D. P. (2003). Chord segmentation and recognition using EM-trained hidden Markov models. In Proceedings of the 4th International Conference on Music Information Retrieval, pages 183–189.
- 40Sigtia, S., Boulanger-Lewandowski, N., and Dixon, S. (2015). Audio chord recognition with a hybrid recurrent neural network. In Proceedings of the 16th International Society for Music Information Retrieval Conference, pages 127–133.
- 41Temperley, D. and Sleator, D. (1999). Modeling meter and harmony: A preference-rule approach. Computer Music Journal, 23(1):10–27. DOI: 10.1162/014892699559616
- 42Tukey, J. W. (1949). Comparing individual means in the analysis of variance. Biometrics, pages 99–114. DOI: 10.2307/3001913
- 43Wakefield, G. H. (1999). Mathematical representation of joint time-chroma distributions. In Proceedings of SPIE’s International Symposium on Optical Science, Engineering, and Instrumentation, volume 3807, pages 637–646.
International Society for Optics and Photonics . DOI: 10.1117/12.367679 - 44Winograd, T. (1968). Linguistics and the computer analysis of tonal harmony. Journal of Music Theory, 12(1):2–49. DOI: 10.2307/842885
- 45Wu, Y., Feng, X., and Li, W. (2017). MIREX 2017 submission: Automatic audio chord recognition with MIDI-trained deep feature and BLSTM-CRF sequence decoding model. Proceedings of the Music Information Retrieval Evaluation eXchange.