Mathematics and Computation in Music

MCM 2015: Mathematics and Computation in Music pp 173-178 | Cite as

Evaluating Singer Consistency and Uniqueness in Vocal Performances

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9110)

Abstract

Identifying consistent and unique aspects of performances is an important aspect of modeling performance style. This paper presents a detailed analysis of inter-singer differences and intra-singer similarities using support vector machines to predict singer identity of a performance from pitch, timing, and dynamics performance parameters. The analysis was performed on a dataset of 72 recordings of the first verse of Schubert’s “Ave Maria”, the dataset consists of 3 a cappella and 3 accompanied performances by 6 professional and 6 non-professional singers.

Keywords

Singing Music performance Intra-performer similarity Inter-performer differences Classification Discrimination 

References

  1. 1.
    Chaffin, R., Lemieux, A., Chen, C.: “It is different each time I play”: variability in highly prepared musical performance. Music Percept. 24(5), 455–472 (2007)CrossRefGoogle Scholar
  2. 2.
    de Cheveigné, A., Kawahara, H.: Yin, a fundamental frequency estimator for speech and music. J. Acoust. Soc. Am. 111(4), 1917–1930 (2002)CrossRefGoogle Scholar
  3. 3.
    Cook, N.: Performance analysis and Chopin’s mazurkas. Musicae Sci. 11(2), 183–207 (2007)CrossRefGoogle Scholar
  4. 4.
    Devaney, J., Mandel, M., Fujinaga, I.: Characterizing singing voice fundamental frequency trajectories. In: Workshop on Applications of Signal Processing to Audio and Acoustics, pp. 73–76 (2011)Google Scholar
  5. 5.
    Devaney, J., Mandel, M.I., Ellis, D.P.W.: Improving midi-audio alignment with acoustic features. In: Workshop on Applications of Signal Processing to Acoustics and Audio, pp. 45–48 (2009)Google Scholar
  6. 6.
    Glasberg, B.R., Moore, B.C.J.: A model of loudness applicable to time-varying sounds. J. Audio Eng. Soc. 50(5), 331–342 (2002)Google Scholar
  7. 7.
    Gockel, H., Moore, B.C., Carlyon, R.P.: Influence of rate of change of frequency on the overall pitch of frequency-modulated tones. J. Acoust. Soc. Am. 109(2), 701–712 (2001)CrossRefGoogle Scholar
  8. 8.
    Howard, D.: Equal or non-equal temperament in a cappella SATB singing. Logop. Phoniatr. Vocol. 32, 87–94 (2007)CrossRefGoogle Scholar
  9. 9.
    Koren, R., Gingras, B.: Perceiving individuality in harpsichord performance. Front. Psychol. 5(141), 1–13 (2014)Google Scholar
  10. 10.
    Lagrange, M., Ozerov, A., Vincent, E.: Robust singer identication in polyphonic music using melody enhancement and uncertainty-based learning. In: International Society for Music Information Retrieval Conference, pp. 595–600 (2012)Google Scholar
  11. 11.
    Ramirez, R., Maestre, E., Perez, A., Serra, X.: Automatic performer identification in celtic violin audio recordings. J. New Music Res. 40(2), 165–174 (2011)CrossRefGoogle Scholar
  12. 12.
    Spiro, N., Gold, N., Rink, J.: The form of performance: analyzing pattern distribution in select recordings of Chopin’s Mazurka Op. 24 No. 2. Musicae Sci. 14, 23–55 (2010)CrossRefGoogle Scholar
  13. 13.
    Widmer, G., Dixon, S., Goebl, W., Pampalk, E., Tobudic, A.: In search of the Horowitz factor. AI Mag. 24, 111–130 (2003)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.School of MusicThe Ohio State UniversityColumbusUSA

Personalised recommendations