Annals of Computer Science and Information Systems, Volume 18

DOI: http://dx.doi.org/10.15439/2019F316

Citation: Proceedings of the 2019 Federated Conference on Computer Science and Information Systems, M. Ganzha, L. Maciaszek, M. Paprzycki (eds). ACSIS, Vol. 18, pages 349–353 (2019)

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Abstract. In the process of acoustic voice analysis, in this case of singing, it is important that the sound samples contain a stable phase of phonation. Sometimes, however, it is not possible. This study was prepared to determine how big are the differences between the values of the acoustic parameters obtained for the initial phase of phonation and for the stable phase of phonation. The values of acoustic parameters, such as, among others shimmer, jitter, RAP, PPQ, APQ, HNR or SPR were estimated for registered singing samples in the initial phase of phonation and in the middle phase. The analysis were performed over the samples of singing of the vowel 'a' recorded many times for different pitches. In the process of analyzing of the obtained results, it was found that the impact of the selection phase of phonation for analysis is crucial in assessing the singing voice quality.

References

1. E. Półrolniczak and M. Kramarczyk, “Analysis of the signal of singing using the vibrato parameter in the context of choir singers,” Journal of Electronic Science and Technology, vol. 11, no. 4, pp. 417–423, December 2013.
2. J. Godino-Llorente, P. Gomez-Vilda, and M. Blanco-Velasco, “Dimensionality reduction of a pathological voice quality assessment system based on gaussian mixture models and short-term cepstral parameters,” Biomedical Engineering, IEEE Transactions on, vol. 53, no. 10, pp. 1943–1953, Oct 2006. http://dx.doi.org/10.1109/TBME.2006.871883
3. K. Omori, A. Kacker, L. M. Carroll, W. D. Riley, and S. M. Blaugrund, “Singing power ratio: Quantitative evaluation of singing voice quality,” Journal of Voice, vol. 10, no. 3, pp. 228 – 235, 1996. http://dx.doi.org/http://dx.doi.org/10.1016/S0892-1997(96)80003-8. [Online]. Available: http://www.sciencedirect.com/science/article/pii/S0892199796800038
4. Y. Meron and K. Hirose, “Separation of singing and piano sounds.” in ICSLP, 1998.
5. M. Muller, D. P. Ellis, A. Klapuri, and G. Richard, “Signal processing for music analysis,” Selected Topics in Signal Processing, IEEE Journal of, vol. 5, no. 6, pp. 1088–1110, 2011.
6. A. Holzapfel, Y. Stylianou, A. C. Gedik, and B. Bozkurt, “Three dimensions of pitched instrument onset detection,” Audio, Speech, and Language Processing, IEEE Transactions on, vol. 18, no. 6, pp. 1517–1527, 2010.
7. L. Mazurowski, “Computer models for algorithmic music composition,” in Computer Science and Information Systems (FedCSIS), 2012 Federated Conference on. IEEE, 2012, pp. 733–737.
8. K. Jensen, “Envelope model of isolated musical sounds,” in Proceedings of the 2nd COST G-6 Workshop on Digital Audio Effects (DAFx99), 1999.
9. R. M. Alderson, Complete handbook of voice training. Parker Publishing Company, 1979.
10. M. Łazoryszczak and E. Półrolniczak, “Audio database for the as sessment of singing voice quality of choir members,” Elektronika: konstrukcje, technologie, zastosowania, vol. 54, no. 3, pp. 92–96, 2013.
11. E. Półrolniczak and M. Łazoryszczak, “Quality assessment of intonation of choir singers using f0 and trend lines for singing sequence,” Metody Informatyki Stosowanej, pp. 259–268, 2011.
12. E. Półrolniczak and M. Kramarczyk, “Computer analysis of the noise component in the singing voice for assessing the quality of singing,” Przeglad Elektrotechniczny, vol. 91, pp. 79–83, 2015.
13. E. Polrolniczak and M. Kramarczyk, “Formant analysis in assessment of the quality of choral singers,” in Signal Processing: Algorithms, Architectures, Arrangements, and Applications (SPA), 2013, Sept 2013. ISSN 2326-0262 pp. 200–204.
14. P. Zwan and B. Kostek, “System for automatic singing voice recognition,” Journal of the Audio Engineering Society, vol. 56, no. 9, pp. 710–723, 2008.
15. E. H. Buder, “Acoustic analysis of voice quality: A tabulation of algorithms 1902–1990,” Voice quality measurement, pp. 119–244, 2000.
16. P. Boersma and D. Weenink, “Praat: doing phonetics by computer [Computer program],” Version 6.0.43, retrieved 8 September 2018 http://www.praat.org/, 2018.
17. Y. Jadoul, B. Thompson, and B. de Boer, “Introducing Parselmouth: A Python interface to Praat,” Journal of Phonetics, vol. 71, pp. 1–15, 2018. http://dx.doi.org/https://doi.org/10.1016/j.wocn.2018.07.001
18. Y.-L. Shue, P. Keating, C. Vicenik, and K. Yu, “Voicesauce,” p. Program available online at http://www. seas. ucla. edu/spapl/voicesauce/. UCLA, 2009.
19. A. De Cheveigné and H. Kawahara, “Yin, a fundamental frequency estimator for speech and music,” The Journal of the Acoustical Society of America, vol. 111, no. 4, pp. 1917–1930, 2002.
20. E. Yumoto, W. J. Gould, and T. Baer, “Harmonics-to-noise ratio as an index of the degree of hoarseness,” The journal of the Acoustical Society of America, vol. 71, no. 6, pp. 1544–1550, 1982.