Melodic patterns and tonal cadences: Bayesian learning of cadential categories from contrapuntal information

References

• Battaglia, P. W., Jacobs, R. A., & Aslin, R. N. (2003). Bayesian integration of visual and auditory signals for spatial localization. Journal of the Optical Society of America, 20(7), 1391–1397.
   Web of Science ®Google Scholar
• Beers, R. J. V., Sittig, A. C., & Gon, J. J. D. V. D. (1999). Integration of proprioceptive and visual position-information: An experimentally supported model. Journal of Neurophysiology, 81, 1355–1364.
   PubMed Web of Science ®Google Scholar
• Bharucha, J. J., & Stoeckig, K. (1986). Reaction time and musical expectancy: Priming of chords. Journal of Experimental Psychology: Human Perception and Performance, 12(4), 403–410.
   PubMed Web of Science ®Google Scholar
• Bigand, E., Madurell, F., Tillmann, B., & Pineau, M. (1999). Effect of global structure and temporal organization on chord processing. Journal of Experimental Psychology: Human Perception and Performance, 25(1), 184–197.
   Web of Science ®Google Scholar
• Bigand, E., & Parncutt, R. (1999). Perceiving musical tension in long chord sequences. Psychological Research, 62, 237–254.
   PubMed Web of Science ®Google Scholar
• Bigand, E., & Pineau, M. (1997). Global context effects on musical expectancy. Perception & Psychophysics, 59(7), 1098–1107.
   PubMed Web of Science ®Google Scholar
• Bigo, L., Giraud, M., Groult, R., Guiomard-Kagan, N., & Levé, F. (2017). Sketching sonata form structure in selected classical string quartets. Proceedings of the 18th International Society for Music Information Retrieval (ISMIR) (pp. 752–759).
   Google Scholar
• Boltz, M. (1989). Perceiving the end: Effects of tonal relationships on melodic completion. Journal of Experimental Psychology: Human Perception and Performance, 15(4), 749–761.
   PubMed Web of Science ®Google Scholar
• Cambouropoulos, E. (1998). Towards a general computational theory of musical structure. Edinburgh: The University of Edinburgh.
   Google Scholar
• Cambouropoulos, E. (2006). Musical parallelism and melodic segmentation: A computational approach. Music Perception, 23(3), 249–267.
   Web of Science ®Google Scholar
• Caplin, W. (1998). Classical form: A theory of formal functions for the instrumental music of Haydn, Mozart, and Beethoven. New York: Oxford University Press.
   Google Scholar
• Clark, A. (2013). Whatever next? Predictive brains, situated agents, and the future of cognitive science. Behavioral and Brain Sciences, 36(3), 1–73.
   PubMed Web of Science ®Google Scholar
• Collins, T. E. (2011). Improved methods for pattern discovery in music, with applications in automated stylistic composition. Milton Keynes: The Open University.
   Google Scholar
• Conklin, D., & Anagnostopoulou, C. (2006). Segmental pattern discovery in music. INFORMS Journal on Computing, 18(3), 285–293.
   Web of Science ®Google Scholar
• Conklin, D., & Bergeron, M. (2008). Feature set patterns in music. Computer Music Journal, 32(1), 60–70.
   Web of Science ®Google Scholar
• Creel, S. C., Newport, E. L., & Aslin, R. N. (2004). Distant melodies: Statistical learning of nonadjacent dependencies in tone sequences. Journal of Experimental Psychology: Learning, Memory, and Cognition, 30(5), 1119–1130.
   PubMed Web of Science ®Google Scholar
• Deliege, I. (1987). Grouping conditions in listening to music: An approach to Lerdahl & Jackendoff's grouping preference rules. Music Perception: An Interdisciplinary Journal, 4(4), 325–359.
   Web of Science ®Google Scholar
• Dowling, W. J. (1973). Rhythmic groups and subjective chunks in memory for melodies. Perception and Psychophysics, 14, 37–40.
   Web of Science ®Google Scholar
• Duane, B. (2012). Agency and information content in eighteenth- and early nineteenth-century string-quartet expositions. Journal of Music Theory, 56(1), 87–120.
   Web of Science ®Google Scholar
• Duane, B. (2016). Repetition and prominence: The probabilistic structure of melodic and non-melodic lines. Music Perception, 34(2), 152–166.
   Web of Science ®Google Scholar
• Duane, B., & Jakubowski, J. (2018). Harmonic clusters and tonal cadences: Bayesian learning without chord identification. Journal of New Music Research, 47(2), 143–165.
   Web of Science ®Google Scholar
• Durbin, R., Eddy, S. R., Krogh, A., & Mitchison, G. (1999). Biological sequence analysis: Probabilistic models of proteins and nucleic acids. Cambridge: Cambridge University Press.
   Google Scholar
• Ernst, M. O., & Banks, M. S. (2002). Humans integrate visual and haptic information in a statistically optimal fashion. Nature, 415, 429–433.
   PubMed Web of Science ®Google Scholar
• Forth, J. C. (2012). Cognitively-motivated geometric methods of pattern discovery and models of similarity in music. Goldsmiths: University of London.
   Google Scholar
• Friston, K. (2009). The free-energy principle: A rough guide to the brain? Trends in Cognitive Sciences, 13(7), 293–301.
   PubMed Web of Science ®Google Scholar
• Friston, K., Kilner, J., & Harrison, L. (2006). A free energy principle for the brain. Journal of Physiology, 100, 70–87.
   Google Scholar
• Giraud, M., Groult, R., Leguy, E., & Levé, F. (2015). Computational fugue analysis. Computer Music Journal, 39(2), 77–96.
   Web of Science ®Google Scholar
• Giraud, M., Groult, R., & Leve, F. (2013). Subject and counter-subject detection for analysis of the Well-Tempered Clavier fugues. In M. Aramaki, M. Barthet, R. Kronland-Martinet, & S. Ystad (Eds.), From sounds to music and emotions. 9th International Symposium, CMMR 2012, London, UK, June 19–22, 2012, Revised Selected Papers. Lecture Notes in Computer Science. Berlin: Springer-Verlag.
   Google Scholar
• Gjerdingen, R. O. (2007). Music in the Galant style. New York, NY: Oxford University Press.
   Google Scholar
• Gregory, A. H. (1978). Perception of clicks in music. Perception & Psychophysics, 24(2), 171–174.
   PubMedGoogle Scholar
• Hillis, J. M., Watt, S. J., Landy, M. S., & Banks, M. S. (2004). Slant from texture and disparity cues: Optimal cue combination. Journal of Vision, 4, 967–992.
   PubMed Web of Science ®Google Scholar
• Jacobs, R. A. (1999). Optimal integration of texture and motion cues to depth. Vision Research, 39, 3621–3629.
   PubMed Web of Science ®Google Scholar
• Janata, P. (1995). ERP measures assay the degree of expectancy violation of harmonic contexts in music. Journal of Cognitive Neuroscience, 7(2), 153–164.
   PubMed Web of Science ®Google Scholar
• Justus, T. C., & Bharucha, J. J. (2001). Modularity in musical processing: The automaticity of harmonic priming. Journal of Experimental Psychology: Human Perception and Performance, 27(4), 1000–1011.
   PubMed Web of Science ®Google Scholar
• Knill, D. C., & Pouget, A. (2004). The Bayesian brain: The role of uncertainty in neural coding and computation. Trends in Neurosciences, 27(12), 712–719.
   PubMed Web of Science ®Google Scholar
• Knill, D. C., & Saunders, J. A. (2003). Do humans optimally integrate stereo and texture information for judgments of surface slant? Vision Research, 43, 2539–2558.
   PubMed Web of Science ®Google Scholar
• Knopke, I., & Jurgensen, F. (2009). A system for identifying common melodic phrases in the masses of Palestrina. Journal of New Music Research, 38(2), 171–181.
   Web of Science ®Google Scholar
• Koelsch, S., Gunter, T., Friederici, A. D., & Schröger, E. (2000). Brain indices of music processing: “Nonmusicians” are musical. Journal of Cognitive Neuroscience, 12(3), 520–541.
   PubMed Web of Science ®Google Scholar
• Lartillot, O. (2005). Multi-dimensional motivic pattern extraction founded on adaptive redundancy filtering. Journal of New Music Research, 34(4), 375–393.
   Web of Science ®Google Scholar
• Lee, C.-H., Gutierrez, F., & Dou, D. (2011). Calculating feature weights in naive Bayes with Kullback-Leibler measure. 11th IEEE international conference on Data Mining.
   Google Scholar
• Leino, S., Brattico, E., Tervaniemi, M., & Vuust, P. (2007). Representation of harmony rules in the human brain: Further evidence from event-related potentials. Brain Research, 1142, 169–177.
   PubMed Web of Science ®Google Scholar
• Loui, P., Grent-'t-Jong, T., Torpey, D., & Woldorff, M. (2005). Effects of attention on the neural processing of harmonic syntax in Western music. Cognitive Brain Research, 25, 678–687.
   PubMedGoogle Scholar
• Loui, P., & Wessel, D. (2007). Harmonic expectation and affect in Western music: Effects of attention and training. Perception & Psychophysics, 69(7), 1084–1092.
   PubMed Web of Science ®Google Scholar
• Manning, C. D., Raghavan, P., & Schutze, H. (2008). Introduction to information retrieval. Cambridge: Cambridge University Press.
   Google Scholar
• Meredith, D. (2015). Music analysis and point-set compression. Journal of New Music Research, 44(3), 245–270.
   Web of Science ®Google Scholar
• Meredith, D., Lemstrom, K., & Wiggins, G. A. (2002). Algorithms for discovering repeated patterns in multidimensional representations of polyphonic music. Journal of New Music Research, 31(4), 321–345.
   Web of Science ®Google Scholar
• Mitchell, T. M. (1997). Machine learning (1st ed.). New York, NY: WCB/McGraw-Hill.
   Google Scholar
• Pearce, M. T., & Wiggins, G. A. (2004). Improved methods for statistical modelling of monophonic music. Journal of New Music Research, 33(4), 367–385.
   Web of Science ®Google Scholar
• Pearce, M. T., & Wiggins, G. A. (2006). Expectation in melody: The influence of context and learning. Music Perception, 23(5), 377–405.
   Web of Science ®Google Scholar
• Rolland, P.-Y. (1999). Discovering patterns in musical sequences. Journal of New Music Research, 28(4), 334–350.
   Web of Science ®Google Scholar
• Rosner, B. S., & Narmour, E. (1992). Harmonic closure: Music theory and perception. Music Perception: An Interdisciplinary Journal, 9(4), 383–411.
   Web of Science ®Google Scholar
• Saffran, J. R., Johnson, E. K., Aslin, R. N., & Newport, E. L. (1999). Statistical learning of tone sequences by human infants and adults. Cognition, 70, 27–52.
   PubMed Web of Science ®Google Scholar
• Sears, D. (2015). The perception of cadential closure. In P. Berge & M. Neuwirth (Eds.), What is a cadence?: Theoretical and analytical perspectives on cadences in the classical repertoire (pp. 253–285). Ithaca, NY: Leuven University Press.
   Google Scholar
• Sears, D., Caplin, W. E., & McAdams, S. (2014). Perceiving the classical cadence. Music Perception, 31(5), 397–417.
   Web of Science ®Google Scholar
• Sears, D. R. W., Pearce, M. T., Caplin, W. E., & McAdams, S. (2018). Simulating melodic and harmonic expectations for tonal cadences using probabilistic models. Journal of New Music Research, 47(1), 29–52.
   Web of Science ®Google Scholar
• Sears, D. R. W., Arzt, A., Frostel, H., Sonnleitner, R., & Widmer, G. (2017). Modeling harmony with skip-grams. Proceedings of the 18th International Society for Music Information Retrieval (ISMIR) (pp. 332–338).
   Google Scholar
• Sloboda, J. A., & Gregory, A. H. (1980). The psychological reality of musical segments. Canadian Journal of Psychology, 34(3), 274–280.
   PubMed Web of Science ®Google Scholar
• Steinbeis, N., Koelsch, S., & Sloboda, J. A. (2006). The role of harmonic expectancy violations in musical emotions: Evidence from subjective, physiological, and neural responses. Journal of Cognitive Neuroscience, 18(8), 1380–1393.
   PubMed Web of Science ®Google Scholar
• Stoffer, T. H. (1985). Representation of phrase structure in the perception of music. Music Perception: An Interdisciplinary Journal, 3(2), 191–220.
   Web of Science ®Google Scholar
• Tan, N., Aiello, R., & Bever, T. G. (1981). Harmonic structure as a determinant of melodic organization. Memory & Cognition, 9(5), 533–539.
   PubMed Web of Science ®Google Scholar
• Taube, H. (1999). Automatic tonal analysis: Toward the implementation of a music theory workbench. Computer Music Journal, 23(4), 18–32.
   Web of Science ®Google Scholar
• Temperley, D. (2007). Music and probability. Cambridge, MA: The MIT Press.
   Google Scholar
• Temperley, D. (2008). A probabalistic model of melody perception. Cognitive Science: A Multidisciplinary Journal, 32, 418–444.
   PubMed Web of Science ®Google Scholar
• Temperley, D. (2009). A unified probabilistic model for polyphonic music analysis. Journal of New Music Research, 38(1), 3–18.
   Web of Science ®Google Scholar
• Unal, E., Bozkurt, B., & Karaosmanoglu, M. K. (2014). A hierarchical approach to Makam classification of Turkish Makam music using symbolic data. Journal of New Music Research, 43(1), 132–146.
   Web of Science ®Google Scholar
• Vilares, I., & Kording, K. (2011). Bayesian models: The structure of the world, uncertainty, behavior, and the brain. Annals of the New York Academy of Sciences, 1224, 22–39.
   PubMed Web of Science ®Google Scholar