Representations of Timbre
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MPCL Bibliographies: Representations of Timbre
Compiled at the Music Perception and Cognition Laboratory (MPCL) — McGill University.
Updated March 28, 2022.
Alluri, V., & Toiviainen, P. (2010). Exploring perceptual and acoustical correlates of polyphonic timbre. Music Perception, 27(3), 223–242. https://doi.org/10.1525/mp.2010.27.3.223
Alluri, V., & Toiviainen, P. (2012). Effect of enculturation on the semantic and acoustic correlates of polyphonic timbre. Music Perception, 29(3), 297–310. https://doi.org/10.1525/mp.2012.29.3.297
Almeida, A., Schubert, E., Smith, J., & Wolfe, J. (2017). Brightness scaling of periodic tones. Attention, Perception, & Psychophysics, 79(7), 1892–1896. https://doi.org/10.3758/s13414-017-1394-6
Antoine, A., & Miranda, E. R. (2017). Musical Acoustics, Timbre, and Computer-Aided Orchestration Challenges. 151–154.
Antoine, A., Williams, D., & Miranda, E. (2016). Towards a timbral classification system for musical excerpts. 13.
Arnal, L. H., Flinker, A., Kleinschmidt, A., Giraud, A., & Poeppel, D. (2015). Human screams occupy a privileged niche in the communication soundscape. Current Biology, 25(15), 2051–2056. https://doi.org/10.1016/j.cub.2015.06.043
Aucouturier, J., & Pachet, F. (2004). Improving timbre similarity: How high’s the sky ? Journal of Negative Results in Speech and Audio Sciences, 1(1), 1–13.
Aures, W. (1985). Ein Berechnungsverfahren der Rauhigkeit [A roughness calculation method]. Acustica, 58, 268–281.
Bañuelos, D. (2005). Beyond the Spectrum of Music: An Exploration Through Spectral Analysis of Sound Color in the Alban Berg Violin Concerto. University of Wisconsin--Madison.
Bolger, D., & Griffith, N. (2005). Multidimensional timbre analysis of shakuhachi honkyoku. Conference on Interdisciplinary Musicology, Montreal, QC, Canada.
Caclin, A., Brattico, E., Ternaviemi, M., Näätänen, R., Morlet, D., Giard, M. H., & McAdams, S. (2006). Separate neural processing of timbre dimensions in auditory sensory memory. Journal of Cognitive Neuroscience, 18(12), 1959–1972. https://doi.org/10.1162/jocn.2006.18.12.1959
Caclin, A., Giard, M. H., Smith, B., & McAdams, S. (2007). Interactive processing of timbre dimensions: A Garner interference study. Brain Research, 1138(1), 159–170. https://doi.org/10.1016/j.brainres.2006.12.065
Caclin, A., McAdams, S., Smith, B., & Giard, M. H. (2008). Interactive processing of timbre dimensions: An exploration with event-related potentials. Journal of Cognitive Neuroscience, 20(1), 49–64. https://doi.org/10.1162/jocn.2008.20001
Caclin, A., McAdams, S., Smith, B., & Winsberg, S. (2005). Acoustic correlates of timbre space dimensions: A confirmatory study using synthetic tones. Journal of the Acoustical Society of America, 118(1), 471–482. https://doi.org/10.1121/1.1929229
Caetano, M., & Rodet, X. (2010). Automatic timbral morphing of musical instrument sounds by high-level descriptors. International Computer Music Conference, United States.
Carral, S. (2011). Determining the just noticeable difference in timbre through spectral morphing: A trombone example. Acta Acustica United with Acustica, 97(3), 466–476. https://doi.org/10.3813/AAA.918427
Carterette, E. C., & Kendall, R. (1996). Acoustical analyses of natural and emulated orchestral instrument signals. 103–108.
Charbonneau, G. R. (1981). Timbre and the perceptual effects of three types of data reduction. Computer Music Journal, 5(2), 10–19. https://doi.org/10.2307/3679875
Chartrand, J. P., & Belin, P. (2006). Superior voice timbre processing in musicians. Neuroscience Letters, 405(3), 164–167. https://doi.org/10.1016/j.neulet.2006.06.053
Chi, T., Ru, P., & Shamma, S. (2005). Multiresolution spectrotemporal analysis of complex sounds. Journal of the Acoustical Society of America, 118(2), 887–906. https://doi.org/10.1121/1.1945807
Chiasson, F., Traube, C., Lagarrigue, C., & McAdams, S. (2017). Koechlin’s volume: Perception of sound extensity among instrument timbres from different families. Musicae Scientiae, 21(1), 113–131.
Crowder, R. G. (1989). Imagery for musical timbre. Journal of Experimental Psychology: Human Perception and Performance, 15(3), 472–478. https://doi.org/10.1037/0096-1523.18.3.728
Daniel, P., & Weber, R. (1997). Psychoacoustical roughness: Implementation of an optimized model. Acta Acustica United with Acustica, 83(1), 113–123.
David, S. V. (2018). Incorporating behavioral and sensory context into spectro-temporal models of auditory encoding. Hearing Research, 360, 107–123. https://doi.org/10.1016/j.heares.2017.12.021
De Poli, G., & Prandoni, P. (1997). Sonological Models for Timbre Characterization. Journal of New Music Research, 26(2), 170–197. https://doi.org/10.1080/09298219708570724
Donnadieu, S. (1997). Représentation mentale du timbre des sons complexes et effets de contexte [Doctoral thesis]. Université Paris V.
Donnadieu, S., McAdams, S., & Winsberg, S. (1994). Context effects in timbre space. 311–312.
Elhilali, M., Shamma, S., Thorpe, S. J., & Pressnitzer, D. (2007). Models of timbre using spectro-temporal receptive fields: Investigation of coding strategies. 19th International Congress on Acoustics, Madrid, Spain.
Elliott, T. M., Hamilton, L. S., & Theunissen, F. E. (2013). Acoustic structure of the five perceptual dimensions of timbre in orchestral instrument tones. Journal of the Acoustical Society of America, 133(1), 389–404. https://doi.org/10.1121/1.4770244
Erickson, M. L. (2018). Inexperienced listeners’ perception of timbre dissimilarity within and between voice categories. Journal of Voice. https://doi.org/10.1016/j.jvoice.2018.09.012
Freed, D. J. (1990). Auditory correlates of perceived mallet hardness for a set of recorded percussive sound events. Journal of the Acoustical Society of America, 87(1), 311–322. https://doi.org/10.1121/1.399298
Giordano, B. L., & McAdams, S. (2010). Sound source mechanics and musical timbre perception: Evidence from previous studies. Music Perception, 28(2), 155–168. https://doi.org/10.1525/mp.2010.28.2.155
Gordon, J. W. (1987). The perceptual attack time of musical tones. Journal of the Acoustical Society of America, 82(1), 88–105. https://doi.org/10.1121/1.395441
Grey, J. M. (1977). Multidimensional perceptual scaling of musical timbres. Journal of the Acoustical Society of America, 61(5), 1270–1277. https://doi.org/10.1121/1.381428
Grey, J. M. (1978). Timbre discrimination in musical patterns. Journal of the Acoustical Society of America, 64(2), 467–472. https://doi.org/10.1121/1.382018
Grey, J. M., & Gordon, J. W. (1978). Perceptual effects of spectral modifications on musical timbres. Journal of the Acoustical Society of America, 63(5), 1493–1500. https://doi.org/10.1121/1.381843
Gygi, B., Kidd, G., & Watson, C. S. (2007). Similarity and categorization of environmental sounds. Perception and Psychophysics, 69(6), 839–855.
Hajda, J. M. (1999). The effect of time-variant acoustical properties on orchestral instrument timbres [Thesis]. University of California.
Halpern, A. R., Zatorre, R. J., Bouffard, M., & Johnson, J. A. (2004). Behavioral and neural correlates of perceived and imagined musical timbre. Neuropsychologia, 42, 1281–1292. https://doi.org/10.1016/j.neuropsychologia.2003.12.017
Hemery, E., & Aucouturier, J. (2015). One hundred ways to process time, frequency, rate and scale in the central auditory system: A pattern-recognition meta-analysis. Frontiers in Computational Neuroscience, 9(80), 1–18. https://doi.org/10.3389/fncom.2015.00080
Hourdin, C., Charbonneau, G., & Moussa, T. (1997). A multidimensional scaling analysis of musical instruments’ time-varying spectra. Computer Music Journal, 21(2), 40–55. https://doi.org/10.2307/3681107
Iverson, P., & Krumhansl, C. L. (1993). Isolating the dynamic attributes of musical timbre. Journal of the Acoustical Society of America, 94(5), 2595–2603. https://doi.org/10.1121/1.407371
Jensen, K. (1999). Timbre Models of Musical Sounds: From the model of one sound to the model of one instrument [Thesis]. University of Copenhagen.
Jingying, Z., & Lingyun, X. (2017). Analysis of timbre perceptual discrimination for Chinese traditional musical instruments. 1–4. https://doi.org/10.1109/CISP-BMEI.2017.8302123
Jozwik, K. M., Kriegeskorte, N., Storrs, K. R., & Mur, M. (2017). Deep convolutional neural networks outperform feature-based but not categorical models in explaining object similarity judgments. Frontiers in Psychology, 8, 1726. https://doi.org/10.3389/fpsyg.2017.01726
Kendall, R. A. (2004). Musical timbre in triadic contexts. 8th International Conference on Music Perception and Cognition, Evanston, Illinois.
Kendall, R. A., & Carterette, E. C. (1991). Perceptual scaling of simultaneous wind instrument timbres. Music Perception, 8(4), 369–404. https://doi.org/10.2307/40285519
Kendall, R. A., Carterette, E. C., & Hajda, J. M. (1999). Perceptual and Acoustical Features of Natural and Synthetic Orchestral Instrument Tones. Music Perception, 16(3), 327–363.
Kendall, R., & Carterette, E. C. (1996). Difference thresholds for timbre related to spectral centroid. 91–95.
Kokoras, P. A. (2005). Towards a holophonic musical texture. International Conference on Computer Music, Barcelona.
Krimphoff, J., McAdams, S., & Winsberg, S. (1994). Caractérisation du timbre des sons complexes. II. Analyses acoustiques et quantification psychophysique. Journal De Physique, 4(C5), 625–628. https://doi.org/10.1051/jp4:19945134
Kumar, S., Forster, H. M., Bailey, P., & Griffiths, T. D. (2008). Mapping unpleasantness of sounds to their auditory representation. Journal of the Acoustical Society of America, 124(6), 3810–3817. https://doi.org/10.1121/1.3006380
Lakatos, S. (2000). A common perceptual space for harmonic and percussive timbres. Perception and Psychophysics, 62(7), 1426–1439.
Lakatos, S., Scavone, G. P., & Cook, P. R. (2000). Obtaining perceptual spaces for large numbers of complex sounds: Sensory, cognitive, and decisional constraint. 245–250.
Lartillot, O., & Toiviainen, P. (2007). A Matlab toolbox for musical feature extraction from audio. In S. Marchand (Ed.), Proceedings of the 10th International Conference on Digital Audio Effects (DAFx-07) (pp. 237–244). Université de Bordeaux 1.
Lichte, W. H. (1941). Attributes of complex tones. Journal of Experimental Psychology, 28(6), 455–480. https://doi.org/10.1037/h0053526
McAdams, S., & Winsberg, S. (2000). Psychophysical quantification of individual differences in timbre perception. In A. Schick, M. Meis, & C. Reckhardt (Eds.), Contributions to Psychological Acoustics: Results of the 8th Oldenburg Symposium on Psychological Acoustics (pp. 165–182). Bis.
McAdams, S., Winsberg, S., Donnadieu, S., De Soete, G., & Krimphoff, J. (1995). Perceptual scaling of synthesized musical timbres: Common dimensions, specificities, and latent subject classes. Psychological Research-Psychologische Forschung, 58(3), 177–192. https://doi.org/10.1007/Bf00419633
Moffat, D., Ronan, D., & Reiss, J. D. (2015). An evaluation of audio feature extraction toolboxes. 18th Int. Conference on Digital Audio Effects, Trondheim, Norway. https://doi.org/10.13140/RG.2.1.1471.4640
Musil, J. J., Elnusairi, B., & Müllensiefen, D. (2013). Perceptual dimensions of short audio clips and corresponding timbre features. Lecture Notes in Computer Science, 7900, 214–227. https://doi.org/10.1007/978-3-642-41248-6_12
Nanay, B. (2015). Perceptual representation/perceptual content. In M. Matthen (Ed.), The Oxford Handbook of Philosophy of Perception (pp. 153–167). Oxford University Press. https://doi.org/10.1093/oxfordhb/9780199600472.013.004
Nymoen, K., Danielsen, A., & London, J. (2017). Validating attack phase descriptors obtained by the Timbre Toolbox and MIRtoolbox. In Proceedings of the 14th Sound and Music Computing Conference 2017 (pp. 214–219). Aalto University; CRIStin. http://urn.nb.no/URN:NBN:no-58820
Patel, A. D., & Iversen, J. R. (2003). Acoustic and perceptual comparison of speech and drum sounds in the North Indian tabla tradition: An empirical study of sound symbolism (M. J. Solé, D. Recasens, & J. Romero, Eds.; pp. 925–928). International Phonetic Association.
Patil, K., & Elhilali, M. (2015). Biomimetic spectro-temporal features for music instrument recognition in isolated notes and solo phrases. EURASIP Journal on Audio, Speech, and Music Processing, 2015. https://doi.org/10.1186/s13636-015-0070-9
Patil, K., Pressnitzer, D., Shamma, S., & Elhilali, M. (2012). Music in our ears: The biological bases of musical timbre perception. PLOS Computational Biology, 8(11), e1002759. https://doi.org/10.1371/journal.pcbi.1002759
Patranabis, A., Banerjee, K., Midya, V., Chakraborty, S., Sanyal, S., Banerjee, A., Sengupta, R., & Ghosh, D. (2015). Harmonic and timbre analysis of tabla strokes. ArXiv Preprint ArXiv:1510.04880. https://arxiv.org/pdf/1510.04880.pdf
Patterson, R. D. (2000). Auditory images: How complex sounds are represented in the auditory system. Journal of the Acoustical Society of Japan, 21(4), 183–190. https://doi.org/10.1250/ast.21.183
Patterson, R. D., Smith, D. R., van Dinther, R., & Walters, T. C. (2008)Patterson, R. D., Smith, D. R., van Dinther, R., & Walters, T. C. (2008). Size information in the production and perception of communication sounds. In W. A. Yost, A. N. Popper, & R. R. Fay (Eds.), Auditory perception of sound sources (pp. 43–75). Springer. https://doi.org/10.1007/978-0-387-71305-2_3
Peeters, G., Giordano, B. L., Susini, P., Misdariis, N., & McAdams, S. (2011). The Timbre Toolbox: Extracting audio descriptors from musical signals. Journal of the Acoustical Society of America, 130(5), 2902–2916. https://doi.org/10.1121/1.3642604
Piazza, E. A., Theunissen, F. E., Wessel, D., & Whitney, D. (2018). Rapid adaptation to the timbre of natural sounds. Scientific Reports, 8(1), 13826. https://doi.org/10.1038/s41598-018-32018-9
Pitt, M. (1995). Evidence for a central representation of instrument timbre. Perception and Psychophysics, 57(1), 43–55. https://doi.org/10.3758/BF03211849
Pitt, M., & Crowder, R. G. (1992). The role of spectral and dynamic cues in imagery for musical timbre. Journal of Experimental Psychology: Human Perception and Performance, 18(3), 728–738. https://doi.org/10.1037/0096-1523.18.3.728
Plazak, J., Huron, D., & Williams, B. (2010). Fixed average spectra of orchestral instrument tones. Empirical Musicology Review, 5(1), 10–17.
Plazak, J., & McAdams, S. (2017). Perceiving changes of sound-source size within musical tone pairs. Psychomusicology: Music, Mind, and Brain, 27(1), 1–13. https://doi.org/10.1037/pmu0000172
Plomp, R. (1970). Timbre as a multidimensional attribute of complex tones. In R. Plomp & G. F. Smoorenburg (Eds.), Frequency analysis and periodicity detection in hearing (pp. 397–410). Sijthoff.
Pollard, H. F. (1988). Feature analysis and musical timbre. Journal of the Catgut Acoustical Society, 1(1), 16–24.
Pollard, H. F., & Jansson, E. V. (1982). A tristimulus method for the specification of musical timbre. Acta Acustica United with Acustica, 51(3), 162–171.
Preis, A. (1984). An attempt to describe the parameter determining the timbre of steady-state harmonic complex tones. Acustica, 55, 1–13.
Pressnitzer, D., Agus, T., & Suied, C. (2015). Acoustic timbre recognition. In D. Jaeger & R. Jung (Eds.), Encyclopedia of Computational Neuroscience (pp. 128–133). Springer Publishing Company, Incorporated.
Reid, L. C. (2013). Composing timbre spaces, composing timbre in space: An exploration of the possibilities of multidimensional timbre representations and their compositional applications [Stanford University]. http://purl.stanford.edu/kp339nk6272
Robinson, K. (1993). Brightness and octave position: Are changes in spectral envelope and in tone height perceptually equivalent? Contemporary Music Review, 9(1), 83–95. https://doi.org/10.1080/07494469300640361
Robinson, K., & Patterson, R. D. (1995). The duration required to identify the instrument, the octave, or the pitch chroma of a musical note. Music Perception, 13(1), 1–15.
Samson, S. (2003). Neuropsychological studies of musical timbre. Annals of the New York Academy of Sciences, 999, 144–151.
Samson, S., Zatorre, R., & Ramsay, J. O. (1997). Multidimensional scaling of synthetic musical timbre: Perception of spectral and temporal characteristics. Canadian Journal of Experimental Psychology, 51(4), 307–315. https://doi.org/10.1121/1.406009
Sandell, G. J. (1998). Macrotimbre: Contributions of attack and steady state. III, 1881–1882.
Shamma, S. (2000). Physiological basis of timbre perception. In M. Gazzzaniga (Ed.), The New Cognitive Neurosciences (pp. 411–423). MIT Press.
Shamma, S. (2001). On the role of space and time in auditory processing. Trends in Cognitive Sciences, 5(8), 340–348. https://doi.org/10.1016/S1364-6613(00)01704-6
Siddiq, S., Reuter, C., Czedik-Eysenberg, I., & Knauf, D. (2015). Towards the comparability and generality of timbre space studies. 237–240.
Siedenburg, K., Fujinaga, I., & McAdams, S. (2016). A comparison of approaches to timbre descriptors in music information retrieval and music psychology. Journal of New Music Research, 45(1), 27–41. https://doi.org/10.1080/09298215.2015.1132737
Siedenburg, K., & Müllensiefen, D. (2017). Modeling timbre similarity of short music clips. Frontiers in Psychology, 8, 639. https://doi.org/10.3389/fpsyg.2017.00639
Smalley, D. (1997). Spectromorphology: Explaining sound-shapes. Organised Sound, 2(2), 107–126. https://doi.org/10.1017/S1355771897009059
Stepanek, J., & Otcenasek, Z. (1999). Rustle as an attribute of timbre of stationary violin tones. CAS Journal, 3(8).
Stepanek, J., & Otcenasek, Z. (2002). Spectral sources of selected features of violin timbre. 6e Congrès Français d’Acoustique.
Suied, C., Drémeau, A., Pressnitzer, D., & Daudet, L. (2013). Auditory sketches: Sparse representations of sounds based on perceptual models. Lecture Notes in Computer Science, 7900, 154–170. https://doi.org/10.1007/978-3-642-41248-6_9
Terhardt, E. (1978). Psychoacoustic evaluation of musical sounds. Perception and Psychophysics, 23(6), 483–492. https://doi.org/10.3758/BF03199523
Thoret, E., Depalle, P., & McAdams, S. (2017). Perceptually salient regions of the modulation power spectrum for musical instrument identification. Frontiers in Psychology, 8, 587. https://doi.org/10.3389/fpsyg.2017.00587
Toiviainen, P., Kaipainen, M., & Louhivuori, J. (1995). Musical timbre: Similarity ratings correlate with computational feature space distances. Journal of New Music Research, 24(3), 282–298. https://doi.org/10.1080/09298219508570686
Town, S. M., & Bizley, J. K. (2013). Neural and behavioral investigations into timbre perception. Frontiers in Systems Neuroscience, 7(88), 1–14. https://doi.org/10.3389/fnsys.2013.00088
Traube, C., & Depalle, P. (2004). Timbral analogies between vowels and plucked string tones. 4. https://doi.org/10.1109/ICASSP.2004.1326821
van Dinther, R., & Patterson, R. D. (2006). Perception of acoustic scale and size in musical instrument sounds. Journal of the Acoustical Society of America, 120(4), 2158–2176. https://doi.org/10.1121/1.2338295
von Bismarck, G. (1974). Sharpness as an attribute of the timbre of steady sounds. Acta Acustica United with Acustica, 30(3), 159–172.
Wedin, L., & Goude, G. (1972). Dimension analysis of the perception of instrumental timbre. Scandinavian Journal of Psychology, 13(3), 228–240. https://doi.org/10.1111/j.1467-9450.1972.tb00071.x
Wessel, D. L. (1973). Psychoacoustics and music: A report from Michigan State University. PACE: Bulletin of the Computer Arts Society, 30, 1–2.
Wessel, D. L. (1979). Timbre space as a musical control structure. Computer Music Journal, 3(2), 45–52. https://doi.org/10.2307/3680283
Wun, S., Horner, A., & Wu, B. (2014). Effect of spectral centroid manipulation on discrimination and identification of instrument timbres. Journal of the Audio Engineering Society, 62(9), 575–583. https://doi.org/10.17743/jaes.2014.0035