Abstract
We present experimental evidence in support of distributed neural codes for timbre that are implicated in discrimination of musical styles. We used functional magnetic resonance imaging (fMRI) in humans and multivariate pattern analysis (MVPA) to identify activation patterns that encode the perception of rich music audio stimuli from five different musical styles. We show that musical styles can be automatically classified from population codes in bilateral superior temporal sulcus (STS). To investigate the possible link between the acoustic features of the auditory stimuli and neural population codes in STS, we conducted a representational similarity analysis and a multivariate regression-retrieval task. We found that the similarity structure of timbral features of our stimuli resembled the similarity structure of the STS more than any other type of acoustic feature. We also found that a regression model trained on timbral features outperformed models trained on other types of audio features. Our results show that human brain responses to complex, natural music can be differentiated by timbral audio features, emphasizing the importance of timbre in auditory perception.
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References
Norman, K., Polyn, S.M., Detre, G.J., Haxby, J.V.: Beyond mind-reading: multi-voxel pattern analysis of fMRI data. Trends in Cognitive Sciences 10(9), 424–430 (2006)
Staeren, N., Renvall, H., De Martino, F., Goebel, R., Formisano, E.: Sound categories are represented as distributed patterns in the human auditory cortex. Current Biology 19(6), 498–502 (2009)
Kilian-Hütten, N., Valente, G., Vroomen, J., Formisano, E.: Auditory cortex encodes the perceptual interpretation of ambiguous sound. The Journal of Neuroscience 31(5), 1715–1720 (2011)
Lee, Y.-S., Janata, P., Frost, C., Hanke, M., Granger, R.: Investigation of melodic contour processing in the brain using multivariate pattern-based fMRI. NeuroImage 57(1), 293–300 (2011)
Samson, S., Zatorre, R.J.: Melodic and harmonic discrimination following unilateral cerebral excision. Brain and Cognition 7(3), 348–360 (1988)
Bizley, J.K., Walker, K.M.M.: Sensitivity and selectivity of neurons in auditory cortex to the pitch, timbre, and location of sounds. The Neuroscientist 16(4), 453–469 (2010)
Samson, S., Zatorre, R.J.: Contribution of the right temporal lobe to musical timbre discrimination. Neuropsychologia 32(2), 231–240 (1994)
Warren, J.D., Jennings, A.R., Griffiths, T.D.: Analysis of the spectral envelope of sounds by the human brain. NeuroImage 24(4), 1052–1057 (2005)
Meyer, M., Zysset, S., von Cramon, D.Y., Alter, K.: Distinct fMRI responses to laughter, speech, and sounds along the human peri-sylvian cortex. Cognitive Brain Research 24(2), 291–306 (2005)
Buracas, G.T., Boynton, G.M.: Efficient design of event-related fMRI experiments using m-sequences. NeuroImage 16(3), 801–813 (2002)
Cox, R.W.: AFNI: software for analysis and visualization of functional magnetic resonance neuroimages. Computers and Biomedical Research 29(3), 162–173 (1996)
Menon, V.: Neural Correlates of Timbre Change in Harmonic Sounds. NeuroImage 17(4), 1742–1754 (2002)
Hanson, S.J., Matsuka, T., Haxby, J.V.: Combinatorial codes in ventral temporal lobe for object recognition: Haxby (2001) revisited: Is there a face area? Neuroimage (23), 156–166 (2004)
Kriegeskorte, N., Mur, M., Ruff, D., Kiani, P., Bodurka, J., Esteky, H.: Matching categorical object representations in inferior temporal cortex of man and monkey. Neuron (60), 1126–1141 (2008)
Casey, M.A.: Bregman music and auditory python toolbox (January 2012), http://bregman.dartmouth.edu/bregman
Müller, M., Ewert, S., Kreuzer, S.: Making chroma features more robust to timbre changes. In: Proceedings of the International Conference on Acoustics, Speech, and Signal Processing, pp. 1877–1880. IEEE (2009)
Brown, J.C., Puckette, M.S.: An efficient algorithm for the calculation of a constant q transform. Journal of the Acoustical Society of America (92), 2698–2701 (1992)
Logan, B.: Mel frequency cepstral coefficients for music modeling. In: Proceedings of the International Symposium on Music Information Retrieval (2000)
Mitchell, T.M., Shinkareva, S.V., Carlson, A., Chang, K.M., Malave, V.L., Mason, R.A., Just, M.A.: Predicting human brain activity associated with the meanings of nouns. Science 320(5880), 1191–1195 (2008)
Tzanetakis, G., Cook, P.R.: Musical genre classification of audio signals. IEEE Transactions on Speech and Audio Processing 10(5), 293–302 (2002)
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Casey, M., Thompson, J., Kang, O., Raizada, R., Wheatley, T. (2012). Population Codes Representing Musical Timbre for High-Level fMRI Categorization of Music Genres. In: Langs, G., Rish, I., Grosse-Wentrup, M., Murphy, B. (eds) Machine Learning and Interpretation in Neuroimaging. Lecture Notes in Computer Science(), vol 7263. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-34713-9_5
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DOI: https://doi.org/10.1007/978-3-642-34713-9_5
Publisher Name: Springer, Berlin, Heidelberg
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