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SRA: An online research tool for spectral and roughness analysis of sound signals

SRA: An online research tool for spectral and roughness analysis of sound signals. Pantelis N. Vassilakis - DePaul University Chicago, USA MERLOT 2007 New Orleans. At A Glance. Auditory roughness Concept & Models Spectral Analysis New versus old methods SRA a. Outline b. Examples of use.

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SRA: An online research tool for spectral and roughness analysis of sound signals

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  1. SRA: An online research tool for spectral and roughness analysis of sound signals Pantelis N. Vassilakis - DePaul UniversityChicago, USA MERLOT 2007New Orleans MERLOT 2007 – SRA: An online research tool for spectral and roughness analysis of sound signals - Pantelis N Vassilakis - DePaul University

  2. At A Glance • Auditory roughnessConcept & Models • Spectral AnalysisNew versus old methods • SRAa. Outlineb. Examples of use MERLOT 2007 – SRA: An online research tool for spectral and roughness analysis of sound signals - Pantelis N Vassilakis - DePaul University

  3. _ Adding two sine signals with frequencies f1 and f2 results in a complex signal whose amplitude fluctuates between a minimum and a maximum value at a rate equal to |f1-f2|. Auditory Roughness Auditory Roughness: Harsh, raspy sound quality of narrow harmonic intervals. An acoustic/sensory dimension of dissonance. One of the perceptual manifestations of interference, expressed as a function of a complex signal’s spectral distribution; a dimension of timbre _ Amplitude fluctuation rate:a) < ~15 fluctuations/second → Beating b) between ~15 and ~75-150 fluctuations/second → Roughness c) > ~ 150 fluctuations/second → combination tones, envelope pitch, etc.. MERLOT 2007 – SRA: An online research tool for spectral and roughness analysis of sound signals - Pantelis N Vassilakis - DePaul University

  4. Previous roughness calculation models • Daniel & Weber (1997) • Sethares (1998) • Leman (2000) • Pressnitzer, McAdams & Colleagues (1997, 1999a, 1999b, 2000)(auditory periphery mechanisms models) • Helmholtz (1885) • Plomp & Levelt (1965) • Kameoka & • Kyriyagawa (1969a,b) • Hutchinson & • Knopoff (1978) MERLOT 2007 – SRA: An online research tool for spectral and roughness analysis of sound signals - Pantelis N Vassilakis - DePaul University

  5. b1 = 3.5b2 = 5.75 x* = 0.24 s1 = 0.0207 s2 = 18.96 Roughness, frequency separation, and register MERLOT 2007 – SRA: An online research tool for spectral and roughness analysis of sound signals - Pantelis N Vassilakis - DePaul University

  6. Roughness and amplitude • Previous experimental studies: • von Béckésy (1960) • Terhardt (1974) General assumption: Roughness is proportional to A1 * A2 MERLOT 2007 – SRA: An online research tool for spectral and roughness analysis of sound signals - Pantelis N Vassilakis - DePaul University

  7. Amplitude modulation depth versus degree of amplitude fluctuation MERLOT 2007 – SRA: An online research tool for spectral and roughness analysis of sound signals - Pantelis N Vassilakis - DePaul University

  8. Proposed Roughness Calculation Model Roughness estimation model (sine pairs): R = X * Y * Z (Vassilakis, 2001, 2005) X = (Amin*Amax)0.1Dependence of R on the absolute amplitude of the sines (Terhardt, 1974; Vassilakis, 2001) Y = 0.5 [2Amin / (Amin+Amax )]3.11Dependence of R on the relative amplitudes of the sines (von Béckésy, 1960; Terhard, 1974; Vassilakis, 2001) Z =e-b1s(fmax - fmin) – e-b2s(fmax - fmin)[b1 = 3.5;  b2 = 5.75;  s = 0.24/(s1fmin + s2);  s1 = 0.0207;  s2 = 18.96]Dependence of R on relative (frequency difference of the sines) and absolute (frequency of the lower sine) frequencies of the added sines (Kameoka & Kuriyagawa, 1969a&b; Plomp & Levelt, 1965; Sethares, 1998) MERLOT 2007 – SRA: An online research tool for spectral and roughness analysis of sound signals - Pantelis N Vassilakis - DePaul University

  9. Comparison of 3 roughness calculation models MERLOT 2007 – SRA: An online research tool for spectral and roughness analysis of sound signals - Pantelis N Vassilakis - DePaul University

  10. Comparison of 3 roughness calculation models MERLOT 2007 – SRA: An online research tool for spectral and roughness analysis of sound signals - Pantelis N Vassilakis - DePaul University

  11. Spectral Analysis Drawbacks of traditional FFT Frequency and time values returned are forced to fit onto the time-frequency grid defined by the analysis window Frequency/temporal “smearing” and uncertainty on precise energy values MERLOT 2007 – SRA: An online research tool for spectral and roughness analysis of sound signals - Pantelis N Vassilakis - DePaul University

  12. Spectral Analysis Frequency analysis and spectral peak pickingSTFT algorithm based on the Reassigned Bandwidth-Enhanced Additive Model Dual STFT, fine-tuning spectral analysis results _ Frequency: time derivative of the argument (phase) of the complex analytic signal representing a given frequency bin _ Time: frequency derivative of the STFT phase, defining the local group delay (correction that pinpoints the precise excitation time) Theory: Developed by Kodera et al. (1976) Expressed mathematically by Auger & Flandrin (1995) Implemented to sound spectral analysis by Fulop & Fitz (2006a,b; 2007) MERLOT 2007 – SRA: An online research tool for spectral and roughness analysis of sound signals - Pantelis N Vassilakis - DePaul University

  13. SRA onlinehttp://www.acousticslab.org/roughness MERLOT 2007 – SRA: An online research tool for spectral and roughness analysis of sound signals - Pantelis N Vassilakis - DePaul University

  14. Dissonance & Orchestration MERLOT 2007 – SRA: An online research tool for spectral and roughness analysis of sound signals - Pantelis N Vassilakis - DePaul University

  15. Dissonance & Orchestration MERLOT 2007 – SRA: An online research tool for spectral and roughness analysis of sound signals - Pantelis N Vassilakis - DePaul University

  16. Roughness Profile MERLOT 2007 – SRA: An online research tool for spectral and roughness analysis of sound signals - Pantelis N Vassilakis - DePaul University

  17. r=0.422 Roughness & Tension Profiles MERLOT 2007 – SRA: An online research tool for spectral and roughness analysis of sound signals - Pantelis N Vassilakis - DePaul University

  18. r=0.422 r=0.068 Roughness & Tension Profiles MERLOT 2007 – SRA: An online research tool for spectral and roughness analysis of sound signals - Pantelis N Vassilakis - DePaul University

  19. References Auger, F. and Flandrin, P. (1995)."Improving the readability of time frequency and time scale representations by the reassignment method," IEEE Transactions on Signal Processing 43: 1068-1089. von Békésy, G. (1960).Experiments in Hearing. New York: Acoustical Society of America Press (1989). Daniel, P. and Weber, R. (1997)."Psychoacoustical roughness: Implementation of an optimized model," Acustica 83: 113-123. Fitz, K. and Haken, L. (2002). "On the use of time-frequency reassignment in additive sound modeling," Journal of the Audio Engineering Society 50(11): 879-893. Fitz, K., Haken, L., Lefvert, S., Champion, C., and O'Donnell, M. (2003). "Cell-utes and flutter-tongued cats: Sound morphing using Loris and the Reassigned Bandwidth-Enhanced Model," Computer Music Journal 27(4): 44-65. Fulop, S. A. and Fitz, K. (2006a). "Algorithms for computing the time corrected instantaneous frequency (reassigned) spectograms with applications," J. Acoust. Soc. Am. 119(1): 360-371. Fulop, S. A. and Fitz, K. (2006b). "A spectrogram for the twenty-first century," Acoustics Today 2(3): 26-33. Fulop, S.A. and Fitz, K. (2007)."Separation of components from impulses in reassigned spectrograms," J. Acoust. Soc. Am. 121(3): 1510-1518. Helmholtz, H. L. F. 1885 [1954].On the Sensations of Tone as a Physiological Basis for the Theory of Music. 2nd English edition. New York: Dover Publications. [Die Lehre von den Tonempfindungen, 1877. 4th German edition, trans. A. J. Ellis.] Kameoka, A. and Kuriyagawa, M. (1969a)."Consonance theory, part I: Consonance of dyads," J. Acoust. Soc. Am. 45(6): 1451-1459. Kameoka, A. and Kuriyagawa, M. (1969b)."Consonance theory, part II: Consonance of complex tones and its calculation method," J. Acoust. Soc. Am. 45(6): 1460-1469. Kendall, R. A. (2002).Music Experiment Development System (MEDS) 2001B for Windows. Los Angeles: University of California Los Angeles, Department of Ethnomusicology, Program in Systematic Musicology. Plomp, R. and Levelt, W. J. M. (1965)."Tonal consonance and critical bandwidth," J. Acoust. Soc. Am. 38(4): 548-560. Pressnitzer, D. and McAdams, S. (1997)."Influence of Phase Effects on Roughness Modeling," ICMC: International Computer Music Conference, Thessaloniki, Greece, September 1997 Pressnitzer, D. and McAdams, S. (1999a)."Two phase effects on roughness perception, ". J. Acoust. Soc. Am. 105(5): 2773-2782. Pressnitzer, D. and McAdams, S. (1999b).Summation of roughness across frequency regions. In: Dau T, Hohmann V, and Kollmeier B (Eds) Temporal processing in the auditory system: Psychophysics, physiology and models of hearing, pages 105-108. World Scientific Publishing, Singapore. Pressnitzer, D., McAdams, S., Winsberg, S., and Fineberg, J. (2000)." Perception of musical tension for non-tonal orchestral timbres and its relation to psychoacoustic roughness," Perception and Psychophysics, 62(1): 66-80. Sethares, W. A. (1998).Tuning, Timbre, Spectrum, Scale. London: Springer-Verlag. Terhardt, E. (1974). "On the perception of periodic sound fluctuations (roughness)," Acustica 30(4): 201-213. Vassilakis, P. N. (2001).Perceptual and Physical Properties of Amplitude Fluctuation and their Musical Significance. Doctoral Dissertation. Los Angeles: University of California, Los Angeles; Systematic Musicology. Vassilakis P. N. (2005). "Auditory roughness as a means of musical expression," Selected Reports in Ethnomusicology 12 (Perspectives in Systematic Musicology): 119-144. MERLOT 2007 – SRA: An online research tool for spectral and roughness analysis of sound signals - Pantelis N Vassilakis - DePaul University

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