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Perceptually Correlated Parameters of Musical Instrument Tones

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Języki publikacji
EN
Abstrakty
EN
In Western music culture instruments have been developed according to unique instrument acoustical features based on types of excitation, resonance, and radiation. These include the woodwind, brass, bowed and plucked string, and percussion families of instruments. On the other hand, instrument performance depends on musical training, and music listening depends on perception of instrument output. Since musical signals are easier to understand in the frequency domain than the time domain, much effort has been made to perform spectral analysis and extract salient parameters, such as spectral centroids, in order to create simplified synthesis models for musical instrument sound synthesis. Moreover, perceptual tests have been made to determine the relative importance of various parameters, such as spectral centroid variation, spectral incoherence, and spectral irregularity. It turns out that the importance of particular parameters depends on both their strengths within musical sounds as well as the robustness of their effect on perception. Methods that the author and his colleagues have used to explore timbre perception are: 1) discrimination of parameter reduction or elimination; 2) dissimilarity judgments together with multidimensional scaling; 3) informal listening to sound morphing examples. This paper discusses ramifications of this work for sound synthesis and timbre transposition.
Rocznik
Strony
225--238
Opis fizyczny
Bibliogr. 16 poz., tab., wykr.
Twórcy
  • University of Illinois at Urbana-Champaign School of Music and Dept. of Electrical & Computer Engineering Urbana, Illinois USA, jwbeauch@illinois.edu
Bibliografia
  • 1. Beauchamp J.W. (2007), Analysis and Synthesis of Musical Instrument Sounds, [in:] Analysis, Synthesis, and Perception of Musical Sounds: The Sound of Music, J.W. Beauchamp [Ed.], pp. 1-89, Springer.
  • 2. Beauchamp J.W., Bay M. (2008), Timbre transposition based on time-varying spectral analysis of continuous monophonic audio and precomputed spectral libraries (abstract), J. Acoust. Soc. Am., 123, 5, 2, 3805.
  • 3. Beauchamp J.W., Lakatos S. (2002), New spectro-temporal measures of musical instrument sounds used for a study of timbral similarity of rise-time- and centroid-normalized musical sounds, Proc. 7 Int. Conf. on Music Perception and Cognition (ICMPC 7), Sydney, Australia, pp. 592-595.
  • 4. Beauchamp J.W., Horner A.B., Koehn H.-F., Bay M. (2006), Multidimensional scaling analysis of centroid- and attack/decay-normalized musical instrument sounds (abstract), J. Acoust. Soc. Am., 120, 5, 2, 3276.
  • 5. Dai H. (2008), On suppressing unwanted cues via randomization, Perception & Psychophysics, 70, 7, 1379-1382.
  • 6. Donnadieu S. (2007), Mental Representation of the Timbre of Complex Sounds, [in:] Analysis, Synthesis, and Perception of Musical Sounds: The Sound of Music, J.W. Beauchamp [Ed.], pp. 272-319, Springer.
  • 7. Grey J.M. (1977), Multidimensional perceptual scaling of musical timbres, J. Acoust. Soc. Am., 61, 5, 1270-1277.
  • 8. Hajda J.M. (2007), The Effect of Dynamic Acoustical Features on Musical Timbre, [in:] Analysis, Synthesis, and Perception of Musical Sounds: The Sound of Music, J.W. Beauchamp [Ed.], pp. 250-271, Springer.
  • 9. Hall M.D., Beauchamp J.W., Horner A.B., Roche J.M. (2010), Importance of Spectral Detail in Musical Instrument Timbre, Proc. 11th Conf. on Music Perception and Cognition (ICMPC 11), Seattle, Washington, USA, pp. 69-74.
  • 10. Horner A.B., Beauchamp J.W., So R.H.Y. (2006), A Search for Best Error Metrics to Predict Discrimination of Original and Spectrally Altered Musical Instrument Sounds, J. Audio Eng. Soc., 54, 3, 140-156.
  • 11. Luce D., Clark M. Jr. (1967), Physical Correlates of Brass-Instrument Tones, J. Acoust. Soc. Am., 42, 6, 1232-1243.
  • 12. McAdams S., Beauchamp J.W., Meneguzzi S. (1999), Discrimination of musical instrument sounds resynthesized with simplified spectrotemporal parameters, J. Acoust. Soc. Am., 105, 2, 1, 882-897.
  • 13. Miller J.R., Carterette E.C. (1975), Perceptual space for musical structures, J. Acoust. Soc. Am., 58, 3, 711-720.
  • 14. Moore B.C.J., Glasberg B.R., Baer T. (1997), A Model for the Prediction of Thresholds, Loudness, and Partial Loudness, J. Audio Eng. Soc., 45, 4, 224-240.
  • 15. Plomp R. (1970), Timbre as a multidimensional attribute of complex tones, [in:] Frequency Analysis and Periodicity Detection in Hearing, R. Plomp and G.F. Smoorenburg [Eds.], pp. 397-414, A.W. Sijtohoff, Leiden.
  • 16. http://ems.music.uiuc.edu/beaucham/
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-article-BUS8-0020-0015
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