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Название RU

Авторы RU

Аннотация RU
<p>Описывается метод определения последовательности аккордов, со- держащейся в музыкальной звукозаписи. По итогам ежегодного соревнования си- стем распознавания аккордов MIREX Audio Chord Estimation 2012 качество работы предлагаемого метода сопоставимо с результатами других участников. В отличие от большинства других систем, описанная в данной работе система не использует методы машинного обучения.</p>

Ключевые слова RU

Литература RU
1. Bas De Haas W. Improving audio chord transcription by exploiting harmonic and metric knowledge /W. Bas De Haas, J. P. Magalh˜aes, F.Wiering // Proceedings of the 13th International Society for Music Information Retrieval Conference. Porto, Portugal, October 8–12, 2012. – Porto, 2012. – P. 295–300. 2. Brown J. C. Calculation of a constant q spectral transform. / J. C. Brown // Journal of the Acoustical Society of America. – 1991. – Vol. 89, N 1. – P. 425–434. 3. Burgoyne J. A. An expert ground truth set for audio chord recognition and music analysis / J. A. Burgoyne, J. Wild, I. Fujinaga // Proceedings of the 12th International Society for Music Information Retrieval Conference. Miami, USA, October 24–28, 2011. – Miami, 2011. – P. 633–638. 4. Cho T. A feature smoothing method for chord recognition using recurrence plots / T. Cho, J. P. Bello // Proc. of the 12th International Society for Music Information Retrieval Conference, Miami, USA, October 24–28, 2011. – Miami, 2011. – P. 651–656. 5. Cho T. Exploring common variations in state of the art chord recognition systems / T. Cho, R. J. Weiss, J. P. Bello // Proceedings of the 7th Sound and Music Computing Conference (SMC 2010). – Barcelona, Spain, 2010. 6. Cooley J. An algorithm for the machine calculation of complex Fourier series / J. Cooley, J. Tukey // Mathematics of Computation. – 1965. – Vol. 19. – P. 297–301. 7. Glazyrin N. Chord recognition using Prewitt filter and self-similarity / N. Glazyrin, A. Klepinin // Proceedings of the 9th Sound and Music Computing Conference. Copenhagen, Denmark, July 11-14, 2012. – Copenhagen, 2012. – P. 480–485. 8. Humphrey E. J. Learning a robust tonnetz-space transform for automatic chord recognition / E. J. Humphrey, T. Cho, and J. P. Bello // Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), IEEE. Kyoto, Japan, 2012. – Kyoto, 2012. – P. 453–456. 9. Analyzing chroma feature types for automated chord recognition / N. Jiang, P. Grosche, V. Konz, M. M¨uller // Proc. of the AES 42nd International Conference: Semantic Audio. Ilmenau, Germany, 2011, July 22–24. – Ilmenau, 2011. 10. Lerch A. On the requirement of automatic tuning frequency estimation / A. Lerch // Proceedings of ISMIR 2006 7th International Conference on Music Information Retrieval. Victoria, Canada, 8-12 October 2006. – Victoria, 2006. – P. 212–215. 11. Mauch M. Using musical structure to enhance automatic chord transcription / M. Mauch, K. C. Noland, S. Dixon // Proc. of the 10th International Conference on Music Information Retrieval. Kobe, Japan, 26–30 October 2009. – Kobe,2009. – P. 231–236. 12. M¨uller M. Making chroma features more robust to timbre changes / M. M¨uller, S. Ewert, S. Kreuzer // Proceedings of the 2009 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP ’09, Washington, DC, USA. – IEEE Computer Society, 2009. – P. 1877–1880. 13. RWC Music Database: Popular, Classical, and Jazz Music Databases / T. Nishimura, M. Goto, H. Hashiguchi, R. Oka // Proceedings of the 3rd International Conference on Music Information Retrieval (ISMIR 2002). Paris, France, October 13-17, 2002. – Paris, 2002. – P. 287–288. 14. Oudre L. Chord recognition using measures of fit, chord templates and filtering methods / L. Oudre, Y. Grenier, C. F´evotte // Proceedings of the IEEE Workshop on Applications of Signal Processing to Audio and Acoustics (WASPAA). New York, USA, 2009. – N. Y., 2009. – P. 9–12. 15. Zhu Y. Music key detection for musical audio / Y. Zhu, M. S. Kankanhalli, S. Gao // Proceedings of 11th International Multimedia Modelling Conference (MMM’05). Melbourne, Australia, January 12-14, 2005. – Melbourne, 2005. – P. 30–37.

Название EN

Авторы EN

Аннотация EN
<p>This paper describes a method of audio chord recognition. In the annual MIREX Audio Chord Esitmation 2012 contest this method achieved results comparable to other participants’ results. It does not employ any machine learning algorithms, unlike majority of other systems.</p>

Ключевые слова EN

Литература EN