A Study on of Music Features Derived from Audio Recordings Examples – a Quantitative Analysis
Abstract
The paper presents a comparative study of music features derived from audio recordings, i.e. the same music pieces but representing different music genres, excerpts performed by different musicians, and songs performed by a musician, whose style evolved over time. Firstly, the origin and the background of the division of music genres were shortly presented. Then, several objective parameters of an audio signal were recalled that have an easy interpretation in the context of perceptual relevance. Within the study parameter values were extracted from music excerpts, gathered and compared to determine to what extent they are similar within the songs of the same performer or samples representing the same piece.Keywords:
music genres, audio parametrization, music featuresReferences
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2. Barthet M., Fazekas G., Allik A., Thalmann F.B., Sandler M. (2016), From interactive to adaptive mood-based music listening experiences in social or personal contexts, Journal of the Audio Engineering Society, 64, 9, 673–682, doi.org/10.17743/jaes.2016.0042.
3. Benward B., Saker M. (2003), Music in theory and practice, Vol. I, p. 12, 7th ed., McGraw-Hill.
4. Bergstra J., Casagrande N., Erhan D., Eck D., Kegl B. (2006), Aggregate features and AdaBoost for music classification, Machine Learning, 65, 2/3, 473– 484, http://dx.doi.org/10.1007/s10994-006-9019-7.
5. Bhalke D.G., Rajesh B., Bormane D.S. (2017), Automatic genre classification using fractional fourier transform based mel frequency cepstral coefficient and timbral features, Archives of Acoustics, 42, 2, 213–222, https://doi.org/10.1515/aoa-2017-002.
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12. Holzapfel A., Stylianou Y. (2008), Musical genre classification using nonnegative matrix factorization- based features, IEEE Transactions on Audio, Speech, and Language Processing, 16, 2, 424–434, http://dx.doi.org/10.1109/TASL.2007.909434.
13. Kalliris G.M., Dimoulas C.A., Uhle C. (2016), Guest Editors’ note. Special issue on intelligent audio processing, semantics, and interaction, Journal of the Audio Engineering Society, 64, 7/8, 464–465.
14. Kostek B. (2005), Perception-based data processing in acoustics. Applications to music information retrieval and psychophysiology of hearing, Series on Cognitive Technologies, Springer Verlag, Berlin, Heidelberg, New York.
15. Kostek B., Kaczmarek A. (2013), Music recommendation based on multidimensional description and similarity measures, Fundamenta Informaticae, 127, 1–4, 325–340, http://dx.doi.org/10.3233/FI-2013-912.
16. Kostek B., Kupryjanow A., Zwan P., Jiang W., Ras Z., Wojnarski M., Swietlicka J. (2011), Report of the ISMIS 2011 contest: music information retrieval, foundations of intelligent systems, ISMIS 2011, LNAI 6804, pp. 715–724, M. Kryszkiewicz et al. [Eds.], Springer Verlag, Berlin, Heidelberg.
17. Kotsakis R., Kalliris G., Dimoulas C. (2012), Investigation of broadcast-audio semantic analysis scenarios employing radio-programme-adaptive pattern classification, Speech Communication, 54, 6, 743–762.
18. Lane D.M. (2017), Difference between two means (Independent Groups), http://onlinestatbook.com/2/. tests of means/difference means.html
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22. Pascall R. (2001), The new Grove dictionary of music and musicians, S. Sadie, J. Tyrrell [Eds.], 24, 2/London, pp. 638–642.
23. Plewa M., Kostek B. (2015), Music mood visualization using self-organizing maps, Archives of Acoustics, 40, 4, 513–525, https://doi.org/10.1515/aoa-2015-0051.
24. Pluta M., Spalek L.J., Delekta R.J. (2017), An automatic synthesis of musical phrases from multipitch samples, Archives of Acoustics, 42, 2, 235–247, https://doi.org/10.1515/aoa-2017-0026.
25. Reljin N., Pokrajac D. (2017), Music performers classification by using multifractal features: a case study, Archives of Acoustics, 42, 2, 223–233, https://doi.org/10.1515/aoa-2017-0025.
26. RockSound (2016), http://www.rocksound.tv/news/. read/study-green-day-blink-182-are-punk-mychemical- romance-are-emo (retrieved 01.13.2016).
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28. Rosner A., Schuller B., Kostek B. (2014), An classification of music genres based on music separation into harmonic and drum components, Archives of Acoustics, 39, 4, 629–638, https://doi.org/10.2478/aoa-2014-0068.
29. Schedl M., Gómez E., Urba J. (2014), Music information retrieval: recent developments and applications, Foundations and Trendsr in Information Retrieval, 8, 2–3, 127–261, https://doi.org/10.1561/1500000042.
30. Seidel W., Leisinger U. (1998), Music in History and the Present [in German: Die Musik in Geschichte und Gegenwart], article: Stil, [Ed.] L. Finscher, pp. 1740–1759, B¨arenreiter and Metzler, Kassel.
31. Silla C.N., Kaestner C.A., Koerich A.L. (2007), Automatic music genre classification using ensemble of classifiers, IEEE International Conference on Systems, Man and Cybernetics, pp. 1687–1692.
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34. Tekman H.G., Hortacsu N. (2002), Aspects of stylistic knowledge: what are different styles like and why do we listen to them?, Psychology of Music, 30, 1, 28–47.
35. Tzanetakis G., Essl G., Cook P. (2002), Automatic musical genre classification of audio signals, IEEE Transactions on Speech and Audio Processing, 10, 5, 293–302.
