References
- Baars, B. J. (1988). A cognitive theory of consciousness. Cambridge University Press.
- Balzano, G. J. (1977). Chronometric Studies of the Musical Interval Sense. PhD thesis, Stanford University.
- Bernardes, G., Cocharro, D., Caetano, M., Guedes, C., & Davies, M. E. (2016). A multilevel tonal interval space for modelling pitch relatedness and musical consonance. Journal of New Music Research, 45(4), 281–294. DOI: 10.1080/09298215.2016.1182192
- Brachman, R. J., & Levesque, H. J. (Eds.) (1985). Readings in Knowledge Representation. San Francisco, CA: Morgan Kaufmann.
- Brachman, R., & Levesque, H. (1992). Knowledge Representation. London: MIT Press.
- Chew, E. (2014).
Mathematical and Computational Modeling of Tonality: Theory and Applications, volume 204 of International Series on Operations Research and Management Science . New York, NY: Springer. DOI: 10.1007/978-1-4614-9475-1_1 - Christensen, T., & Rameau, J.-P. (1987). Eighteenth-century science and the “corps sonore:” the scientific background to rameau’s “principle of harmony”. Journal of Music Theory, 31(1), 23–50. DOI: 10.2307/843545
- Davis, S., & Mermelstein, P. (1980). Comparison of parametric representations for monosyllabic word recognition in continuously spoken sentences. IEEE Transactions on Acoustics, Speech, and Signal Processing, 28(4), 357–366. DOI: 10.1109/TASSP.1980.1163420
- Forth, J., Agres, K., Purver, M., & Wiggins, G. A. (2016). Entraining IDyOT: timing in the information dynamics of thinking. Frontiers in Psychology, 7, 1575. DOI: 10.3389/fpsyg.2016.01575
- Friston, K. (2010). The free-energy principle: A unified brain theory? Nature Reviews Neuroscience, 11(2), 127–138. DOI: 10.1038/nrn2787
- Friston, K. J. (2011). Functional and effective connectivity: a review. Brain connectivity, 1(1), 13–36. DOI: 10.1089/brain.2011.0008
- Friston, K. J., Kahan, J., Razi, A., Stephan, K. E., & Sporns, O. (2014). On nodes and modes in resting state fmri. NeuroImage, 99, 533–547. DOI: 10.1016/j.neuroimage.2014.05.056
- Gärdenfors, P. (2000). Conceptual Spaces: the geometry of thought. Cambridge, MA: MIT Press. DOI: 10.7551/mitpress/2076.001.0001
- Groschner, L. N., Malis, J. G., Zuidinga, B., & Borst, A. (2022). A biophysical account of multiplication by a single neuron. Nature, 603(7899), 119–123. DOI: 10.1038/s41586-022-04428-3
- Harrison, P. M. C., & Pearce, M. T. (2018). An energy-based generative sequence model for testing sensory theories of Western harmony.
- Harte, C., Sandler, M., & Gasser, M. (2006).
Detecting harmonic change in musical audio . In Proceedings of the 1st ACM Workshop on Audio and Music Computing Multimedia, pages 21–26, New York. ACM. DOI: 10.1145/1178723.1178727 - Hedges, T. W. (2017). Advances in Multiple Viewpoint Systems and Applications in Modelling Higher Order Musical Structure. PhD thesis, Queen Mary University of London.
- Hedges, T. W., & Wiggins, G. A. (2016). Improving predictions of derived viewpoints in multiple viewpoint systems. In Proceedings of ISMIR 2016.
- Helmholtz, H. (1954). On the Sensations of Tone as a Physiological Basis for the Theory of Music. Dover, New York, second edition. Translated by A. Ellis (originally published 1877).
- Homer, S. T., Harley, N., & Wiggins, G. A. (forthcoming). The Discrete Resonance Spectrogram: a novel method for precise determination of spectral content. In preparation.
- Hoppensteadt, F. C., & Izhikevich, E. M. (1996). Synaptic organizations and dynamical properties of weakly connected neural oscillators. Biological Cybernetics, 75(2), 117–127. DOI: 10.1007/s004220050279
- Kennedy, R. A., & Sadeghi, P. (2013). Hilbert Space Methods in Signal Processing. Cambridge, UK: Cambridge University Press. DOI: 10.1017/CBO9780511844515
- Klapuri, A. (2006). Multiple fundamental frequency estimation by summing harmonic amplitudes. In Proceedings of ISMIR 2006.
- Kraus, N., & Nicol, T. (2019).
Brainstem encoding of speech and music sounds in humans . In The Oxford Handbook of the Auditory Brainstem, pages (on–line version). Oxford University Press. DOI: 10.1093/oxfordhb/9780190849061.013.26 - Krumhansl, C. L., & Kessler, E. J. (1982). Tracing the dynamic changes in perceived tonal organisation in a spatial representation of musical keys. Psychological Review, 89(4), 334–368. DOI: 10.1037/0033-295X.89.4.334
- Krumhansl, C. L., & Shepard, R. N. (1979). Quantification of the hierarchy of tonal functions within a diatonic context. Journal of Experimental Psychology: Human Perception and Performance, 5(4), 579–594. DOI: 10.1037//0096-1523.5.4.579
- Large, E. W. (2006).
A generic nonlinear model for auditory perception . In A. L. Nuttall, T. Ren, P. Gillespie, K. Grosh & E. de Boer (Eds.), Auditory Mechanisms: Processes and Models, pages 516–517. Singapore: World Scientific. DOI: 10.1142/9789812773456_0087 - Large, E. W., Almonte, F., & Velasco, M. (2010). A canonical model for gradient frequency neural networks. Physica D. DOI: 10.1016/j.physd.2009.11.015
- Lerdahl, F. (2001). Tonal Pitch Space. Oxford: Oxford University Press.
- Lerud, K. D., Kim, J. C., Almonte, F. V., Carney, L. H., & Large, E. W. (2019a). A canonical oscillator model of cochlear dynamics. Hearing Research, 380, 100–107. DOI: 10.1016/j.heares.2019.06.001
- Lerud, K. D., Kim, J. C., Almonte, F. V., Carney, L. H., & Large, E. W. (2019b). A canonical oscillator model of cochlear dynamics. Hearing Research, 380, 100–107. DOI: 10.1016/j.heares.2019.06.001
- Lindeberg, T., & Friberg, A. (2015). Idealized computational models for auditory receptive fields. PLOS ONE, 10(3), 1–58. DOI: 10.1371/journal.pone.0119032
- Longuet-Higgins, H. C. (1962a). Letter to a musical friend. The musical review, 23, 244–8, 271–80.
- Longuet-Higgins, H. C. (1962b). Second letter to a musical friend. The Music Review, 23, 271–280.
- Mallat, S. G. (2009). A Wavelet Tour of Signal Processing: The Sparse Way. Elsevier/Academic Press, 3rd ed edition.
- Mehotra, K., Mohan, C. K., & Ranka, S. (1996). Elements of Artificial Neural Networks. Bradford Books. DOI: 10.7551/mitpress/2687.001.0001
- Milne, A. (2013). A Computational Model of the Cognition of Tonality. PhD thesis, The Open University.
- Milne, A. J., & Holland, S. (2016). Empirically testing tonnetz, voice-leading, and spectral models of perceived triadic distance. Journal of Mathematics and Music, 10(1), 59–85. DOI: 10.1080/17459737.2016.1152517
- Milne, A. J., Laney, R., & Sharp, D. B. (2015). A spectral pitch class model of the probe tone data and scalic tonality. Music Perception, 32(4), 364–393. DOI: 10.1525/mp.2015.32.4.364
- Milne, A. J., Prechtl, A., Laney, R., & Sharp, D. B. (2010). Spectral pitch distance and microtonal melodies. In Proceedings of the International Conference of Music Perception and Cognition 2010 (ICMPC 11).
- Milne, A. J., Sethares, W. A., Laney, R., & Sharp, D. B. (2011). Modelling the similarity of pitch collections with expectation tensor. Journal of Mathematics and Music, 5(1), 1–20. DOI: 10.1080/17459737.2011.573678
- Muggleton, S. (1991). Inductive logic programming. New Generation Computing, 8(4), 295–318. DOI: 10.1007/BF03037089
- Needham, T. (2021). Visual Differential Geometry and Forms: A Mathematical Drama in Five Acts. Princeton University Press. DOI: 10.1515/9780691219899
- Oppenheim, A. V. & Schafer, R. W. (2010). Discrete-Time Signal Processing. Pearson, 3rd ed edition.
- Parncutt, R. (2011). The tonic as triad: Key profiles as pitch salience profiles of tonic triads. Music Perception, 28(4), 333–366. DOI: 10.1525/mp.2011.28.4.333
- Pearce, M. T. (2005). The Construction and Evaluation of Statistical Models of Melodic Structure in Music Perception and Composition. PhD thesis, London, London, UK: Department of Computing, City University.
- Pearce, M. T., Müllensiefen, D., & Wiggins, G. A. (2010). The role of expectation and probabilistic learning in auditory boundary perception: A model comparison. Perception, 39(10), 1367–1391. DOI: 10.1068/p6507
- Pearce, M. T., & Wiggins, G. A. (2012). Auditory expectation: The information dynamics of music perception and cognition. Topics in Cognitive Science, 4(4), 625–652. DOI: 10.1111/j.1756-8765.2012.01214.x
- Rameau, J.-P. (1722). Traité de l’harmonie réduite à ses principes naturels. Ballard, Paris.
- Rumelhart, D. E., & McClelland, J. L. (1986). Parallel Distributed Processing: Exploration in the Microstructure of Cognition. Cambridge, MA: MIT Press. Volumes 1 and 2. DOI: 10.7551/mitpress/5236.001.0001
- Saenz, M., & Langers, D. R. (2014).
Tonotopic mapping of human auditory cortex . Hearing Research, 307, 42–52. Human Auditory NeuroImaging. DOI: 10.1016/j.heares.2013.07.016 - Shannon, C. (1948). A mathematical theory of communication. Bell System Technical Journal, 27, 379–423, 623–56. DOI: 10.1002/j.1538-7305.1948.tb00917.x
- Shepard, R. N. (1982). Geometrical approximations to the structure of musical pitch. Psychological Review, 89(4): 305–333. DOI: 10.1037//0033-295X.89.4.305
- Skoe, E., & Kraus, N. (2010). Auditory brain stem response to complex sounds: a tutorial. Ear and hearing, 31(3), 302–324. DOI: 10.1097/AUD.0b013e3181cdb272
- Spivey, M. (2008). The Continuity of Mind. Oxford University Press.
- Strogatz, S. H. (2015). Nonlinear Dynamics and Chaos: With Applications to Physics, Biology, Chemistry, and Engineering. Westview Press, a member of the Perseus Books Group, Boulder, CO, second edition edition.
- Vernon, D. (2014). Artificial Cognitive Systems: A Primer. Cambridge, MA: MIT Press.
- Ward, L. M., & Greenwood, P. E. (2007). 1/f noise. Scholarpedia, 2(12), 1537. revision #137265. DOI: 10.4249/scholarpedia.1537
- Weiss, T. F. (1996). Cellular Biophysics. Cambridge, MA, USA: MIT Press.
- Wiggins, G. A. (2011).
Computer models of (music) cognition . In P. Rebuschat, M. Rohrmeier, I. Cross & J. Hawkins (Eds.), Language and Music as Cognitive Systems, pages 169–188. Oxford: Oxford University Press. DOI: 10.1093/acprof:oso/9780199553426.003.0018 - Wiggins, G. A. (2012). The mind’s chorus: Creativity before consciousness. Cognitive Computation, 4(3), 306–319. DOI: 10.1007/s12559-012-9151-6
- Wiggins, G. A. (2020). Creativity, information, and consciousness: the information dynamics of thinking. Physics of Life Reviews, 34–35:1–39. DOI: 10.1016/j.plrev.2018.05.001
- Wiggins, G. A., & Forth, J. C. (2015).
IDyOT: A computational theory of creativity as everyday reasoning from learned information . In T. R. Besold, M. Schorlemmer & A. Smaill (Eds.), Computational Creativity Research: Towards Creative Machines, Atlantis Thinking Machines, pages 127–150. Atlantis/Springer. DOI: 10.2991/978-94-6239-085-0_7 - Wiggins, G. A., & Sanjekdar, A. (2019). Learning and consolidation as re-representation: revising the meaning of memory. Frontiers in Psychology: Cognitive Science, 10(802). DOI: 10.3389/fpsyg.2019.00802
