“References” in “Connectionist Representations of Tonal Music”
References
Ackley, D. H., Hinton, G. E., & Sejnowski, T. J. (1985). A learning algorithm for Boltzman machines. Cognitive Science, 9, 147–169.
Adiloglu, K., & Alpaslan, F. N. (2007). A machine learning approach to two-voice counterpoint composition. Knowledge-Based Systems, 20(3), 300–309. doi: 10.1016/j.knosys.2006.04.018
Albrecht, J. D., & Shanahan, D. (2013). The use of large corpora to train a new type of key-finding algorithm: An improved treatment of the minor mode. Music Perception, 31(1), 59–67. doi: 10.1525/mp.2013.31.1.59
Allen, D. (1967). Octave discriminability of musical and non-musical subjects. Psychonomic Science, 7, 421–422.
Amit, D. J. (1989). Modeling brain function: The world of attractor neural networks. Cambridge, UK: Cambridge University Press.
Anderson, J. A. (1995). An introduction to neural networks. Cambridge, MA: MIT Press.
Atkinson, R. C., Bower, G. H., & Crothers, E. J. (1965). An introduction to mathematical learning theory. New York, NY: John Wiley & Sons.
Babbitt, M. (1960). Twelve-tone invariants as compositional determinants. The Musical Quarterly, 46(2), 246–259.
Babbitt, M. (1961). Set structure as a compositional determinant. Journal of Music Theory, 5(1), 72–94.
Baesens, B., Setiono, R., Mues, C., & Vanthienen, J. (2003). Using neural network rule extraction and decision tables for credit-risk evaluation. Management Science, 49(3), 312–329. doi: 10.1287/mnsc.49.3.312.12739
Ballard, D. (1986). Cortical structures and parallel processing: Structure and function. Journal of Music Theory, 21(2), 293–323.
Barbour, J. M. (1972). Tuning and temperament: A historical survey. New York, NY: Da Capo Press.
Barlow, H. B. (1972). Single units and sensation: A neuron doctrine for perceptual psychology? Perception, 1, 371–394.
Barlow, H.B. (1995). The neuron doctrine in perception. In M. S. Gazzaniga (Ed.), The cognitive neurosciences (pp. 415–435). Cambridge, MA: MIT Press.
Bechtel, W. (1994). Natural deduction in connectionist systems. Synthese, 101, 433–463.
Bechtel, W., & Abrahamsen, A. A. (2002). Connectionism and the mind: Parallel processing, dynamics, and evolution in networks (2nd ed.). Malden, MA: Blackwell.
Bellgard, M. I., & Tsang, C. P. (1994). Harmonizing music the Boltzmann way. Connection Science, 6, 281–297.
Bengio, Y., Courville, A., & Vincent, P. (2013). Representation learning: A review and new perspectives. IEEE Transactions on Pattern Analysis and Machine Intelligence, 35(8), 1798–1828. doi: 10.1109/tpami.2013.50
Benuskova, L. (1994). Modeling the effect of the missing fundamental with an attractor neural network. Network: Computation in Neural Systems, 5(3), 333–349.
Benuskova, L. (1995). Modeling transpositional invariancy of melody recognition with an attractor neural network. Network: Computation in Neural Systems, 6(3), 313–331.
Berkeley, I., & Raine, R. (2011). An old-fashioned connectionist approach to a Cajun chord change problem. Connection Science, 23(3), 209–218. doi: 10.1080/09540091.2011.597500
Berkeley, I. S. N., Dawson, M. R. W., Medler, D. A., Schopflocher, D. P., & Hornsby, L. (1995). Density plots of hidden value unit activations reveal interpretable bands. Connection Science, 7, 167–186.
Berkeley, I. S. N., & Gunay, C. (2004). Conducting banding analysis with trained networks of sigmoid units. Connection Science, 16(2), 119–128. doi: 10.1080/09540090412331282278
Berkowitz, A.L. (2010). The improvising mind: Cognition and creativity in the musical moment. New York, NY: Oxford University Press.
Bertalanffy, L.v. (1967). Robots, men, and minds. New York, NY: G. Braziller.
Bertalanffy, L.v. (1969). General system theory: Foundations, development, applications. New York, NY: G. Braziller.
Bharucha, J., & Krumhansl, C. L. (1983). The representation of harmonic structure in music: Hierarchies of stability as a function of context. Cognition, 13(1), 63–102. doi: 10.1016/0010-0277(83)90003tw
Bharucha, J. J. (1987). Music cognition and perceptual facilitation: A connectionist framework. Music Perception, 5(1), 1–30.
Bharucha, J. J. (1999). Neural nets, temporal composites, and tonality. In D. Deutsch (Ed.), The psychology of music (2nd ed., pp. 413–440). San Diego, CA: Academic Press.
Bharucha, J. J., & Todd, P. M. (1989). Modeling the perception of tonal structure with neural nets. Computer Music Journal, 13(4), 44–53.
Bidelman, G. M., & Krishnan, A. (2009). Neural correlates of consonance, dissonance, and the hierarchy of musical pitch in the human brainstem. Journal of Neuroscience, 29(42), 13165–13171. doi: 10.1523/jneurosci.3900-09.2009
Bishop, C. M. (1995). Neural networks for pattern recognition. New York, NY: Oxford University Press.
Blackwell, H. R., & Schlosberg, H. (1943). Octave generalization, pitch discrimination, and loudness thresholds in the white rat. Journal of Experimental Psychology, 33(5), 407–419. doi: 10.1037/h0057863
Boole, G. (1854/2003). The laws of thought. Amherst, NY: Prometheus Books (Originally published in 1854).
Boring, E. G. (1950). A history of experimental psychology. New York, NY: Appleton-Century-Crofts.
Braitenberg, V. (1984). Vehicles: Explorations in synthetic psychology. Cambridge, MA: MIT Press.
Brown, H., & Butler, D. (1981). Diatonic trichords as minimal tonal cue-cells. In Theory Only, 5(6 & 7), 37–55.
Browne, R. (1981). Tonal implications of the diatonic set. In Theory Only, 5(6–7), 3–21.
Broze, Y., & Shanahan, D. (2013). Diachronic changes in jazz harmony: A cognitive perspective. Music Perception, 31(1), 32–45. doi: 10.1525/mp.2013.31.1.32
Bruhn, S. (2014). J. S. Bach’s well-tempered clavier: In-depth analysis and interpretation. Waldkirch, Germany: Edition Gorz.
Bruner, J. S. (1990). Acts of meaning. Cambridge, MA: Harvard University Press.
Bugatti, A., Flammini, A., & Migliorati, P. (2002). Audio classification in speech and music: A comparison between a statistical and a neural approach. Eurasip Journal on Applied Signal Processing, 2002(4), 372–378.
Burnod, Y. (1990). An adaptive neural network: The cerebral cortex. London, UK: Prentice-Hall.
Buus, S., Lauemoller, S.L., Worning, P., Kesmir, C., Frimurer, T., Corbet, S., . . . Brunak, S. (2003). Sensitive quantitative predictions of peptide-MHC binding by a ’Query by Committee’ artificial neural network approach. Tissue Antigens, 62(5), 378–384. doi: 10.1034/j.1399-0039.2003.00112.x
Butler, D. (1989). Describing the perception of tonality in music: A critique of the tonal hierarchy theory and a proposal for a theory of intervallic rivalry. Music Perception, 6(3), 219–242.
Cangelosi, A. (2010). Connectionist modelling of music emotions. Physics of Life Reviews, 7(1), 37–38. doi: 10.1016/j.plrev.2010.01.005
Carpenter, G. A., & Grossberg, S. (1992). Neural networks for vision and image processing. Cambridge, MA: MIT Press.
Caudill, M., & Butler, B. (1992). Understanding neural networks (Vol.1). Cambridge, MA: MIT Press.
Chalmers, J. (1992). Divisions of the tetrachord: A prolegomenon to the construction of musical scales. Lebanon, NH: Frog Peak Music.
Chomsky, N. (1965). Aspects of the theory of syntax. Cambridge, MA: MIT Press.
Churchland, P. S. (1986). Neurophilosophy. Cambridge, MA: MIT Press.
Churchland, P. S., & Sejnowski, T. J. (1992). The computational brain. Cambridge, MA: MIT Press.
Clark, A. (1993). Associative engines. Cambridge, MA: MIT Press.
Claudon, F. (1980). The concise encyclopedia of Romanticism. Secaucus, NJ: Chartwell Books.
Cohen, H. F. (1984). Quantifying music: The science of music at the first stage of the Scientific Revolution, 1580–1650. Boston, MA: D. Reidel.
Conrad, R. (1964). Information, acoustic confusion, and memory span. British Journal of Psychology, 55, 429–432.
Cook, P. R. (1999). Music, cognition and computerized sound. Cambridge, MA: MIT Press.
Coombs, C. H., Dawes, R. M., & Tversky, A. (1970). Mathematical psychology: An elementary introduction. Englewood Cliffs, NJ: Prentice-Hall.
Coutinho, E., & Cangelosi, A. (2009). The use of spatio-temporal connectionist models in psychological studies of musical emotions. Music Perception, 27(1), 1–15. doi: 10.1525/mp.2009.27.1.1
Creighton, H. (1932). Songs and ballads from Nova Scotia. Toronto, ON: J. M. Dent.
Cummins, R. (1983). The nature of psychological explanation. Cambridge, MA: MIT Press.
Cutting, J. E. (1986). Perception with an eye for motion. Cambridge, MA: MIT Press.
Cutting, J. E., & Proffitt, D. (1982). The minimum principle and the perception of absolute, common, and relative motions. Cognitive Psychology, 14, 211–246.
Cynx, J. (1993). Auditory frequency generalization and a failure to find octave generalization in a songbird, the European starling (sturnus vulgaris). Journal of Comparative Psychology, 107(2), 140–146. doi: 10.1037/0735-7036.107.2.140
Das, A., Reddy, N. P., & Narayanan, J. (2001). Hybrid fuzzy logic committee neural networks for recognition of swallow acceleration signals. Computer Methods and Programs in Biomedicine, 64(2), 87–99. doi: 10.1016/s0169-2607(00)00099-7
Dawson, M. R. W. (1998). Understanding cognitive science. Oxford, UK: Blackwell.
Dawson, M. R. W. (2004). Minds and machines: Connectionism and psychological modeling. Malden, MA: Blackwell.
Dawson, M. R. W. (2005). Connectionism: A Hands-on Approach. Oxford, UK: Blackwell.
Dawson, M. R. W. (2008). Connectionism and classical conditioning. Comparative Cognition and Behavior Reviews, 3 (Monograph), 1–115.
Dawson, M. R. W. (2009). Computation, cognition—and connectionism. In D. Dedrick & L. Trick (Eds.), Cognition, computation, and Pylyshyn (pp. 175–199). Cambridge, MA: MIT Press.
Dawson, M. R. W. (2013). Mind, body, world: Foundations of cognitive science. Edmonton, AB: Athabasca University Press.
Dawson, M. R. W., & Boechler, P. M. (2007). Representing an intrinsically nonmetric space of compass directions in an artificial neural network. International Journal of Cognitive Informatics and Natural Intelligence, 1, 53–65.
Dawson, M. R. W., Boechler, P. M., & Orsten, J. (2005). An artificial neural network that uses coarse allocentric coding of direction to represent distances between locations in a metric space. Spatial Cognition and Computation, 5, 29–67.
Dawson, M. R.W., Boechler, P. M., & Valsangkar-Smyth, M. (2000a). Representing space in a PDP network: Coarse allocentric coding can mediate metric and nonmetric spatial judgements. Spatial Cognition and Computation, 2, 181–218.
Dawson, M. R.W., & Dupuis, B. (2012). Equilibria of perceptrons for simple contingency problems. IEEE Transactions on Neural Networks and Learning Systems.
Dawson, M. R. W., Dupuis, B., Spetch, M. L., & Kelly, D. M. (2009). Simple artificial networks that match probability and exploit and explore when confronting a multiarmed bandit. IEEE Transactions on Neural Networks, 20(8), 1368–1371.
Dawson, M. R. W., Dupuis, B., & Wilson, M. (2010a). From bricks to brains: The embodied cognitive science of LEGO robots. Edmonton, AB: Athabasca University Press.
Dawson, M. R. W., & Gupta, M. (2017). Probability matching in perceptrons: Effects of conditional dependence and linear nonseparability. PloS ONE, 12(2), e0172431. doi: doi:10.1371/journal.pone.0172431
Dawson, M. R. W., Kelly, D. M., Spetch, M. L., & Dupuis, B. (2010b). Using perceptrons to explore the reorientation task. Cognition, 114(2), 207–226.
Dawson, M. R. W., Medler, D. A., & Berkeley, I. S. N. (1997). PDP networks can provide models that are not mere implementations of classical theories. Philosophical Psychology, 10, 25–40.
Dawson, M. R. W., Medler, D. A., McCaughan, D. B., Willson, L., & Carbonaro, M. (2000b). Using extra output learning to insert a symbolic theory into a connectionist network. Minds and Machines, 10, 171–201.
Dawson, M. R. W., & Piercey, C. D. (2001). On the subsymbolic nature of a PDP architecture that uses a nonmonotonic activation function. Minds and Machines, 11, 197–218.
Dawson, M. R. W., & Schopflocher, D. P. (1992). Modifying the generalized delta rule to train networks of nonmonotonic processors for pattern classification. Connection Science, 4, 19–31.
Dawson, M. R. W., & Shamanski, K. S. (1994). Connectionism, confusion and cognitive science. Journal of Intelligent Systems, 4, 215–262.
Dawson, M. R. W., & Zimmerman, C. (2003). Interpreting the internal structure of a connectionist model of the balance scale task. Brain & Mind, 4, 129–149.
Deliège, I., & Sloboda, J.A. (1997). Perception and cognition of music. Hove, East Sussex, UK: Psychology Press.
Demany, L., & Armand, F. (1984). The perceptual reality of tone chroma in early infancy. Journal of the Acoustical Society of America, 76(1), 57–66. doi: 10.1121/1.391006
Demsey, D. (1991). Chromatic third relations in the music of John Coltrane. Annual Review Of Jazz Studies, 5, 145–180.
Desain, P., & Honing, H. (1989). The quantization of musical time: A connectionist approach. Computer Music Journal, 13(3), 56–66.
Descartes, R. (1637/2006). A discourse on the method of correctly conducting one’s reason and seeking truth in the sciences (I. Maclean, Trans.). New York, NY: Oxford University Press.
Descartes, R. (1641/1996). Meditations on first philosophy (Rev. ed.). New York, NY: Cambridge University Press.
Deutsch, D. (1982). The psychology of music. New York, NY: Academic Press.
Deutsch, D. (1986). A musical paradox. Music Perception, 3(3), 275–280.
Deutsch, D. (1987). The tritone paradox: Effects of spectral variables. Perception & Psychophysics, 41(6), 563–575. doi: 10.3758/bf03210490
Deutsch, D. (1991). The tritone paradox: An influence of language on music perception. Music Perception, 8(4), 335–347.
Deutsch, D. (1999). The psychology of music (2nd ed.). San Diego, CA: Academic Press.
Deutsch, D. (2010). The paradox of pitch circularity. Acoustics Today, 6(3), 8–15.
Deutsch, D. (2013). The psychology of music (3rd ed.). Waltham, MA: Academic Press.
Deutsch, D., & Boulanger, R.C. (1984). Octave equivalence and the immediate recall of pitch sequences. Music Perception, 2(1), 40–51.
DeVito, C., & Porter, L. (2008). The John Coltrane reference. New York, NY: Routledge.
Dhombres, J. (2002). Lagrange, “working mathematician,” on music considered as a source for science. In G. Assayag, H. G. Feichtinger & J.-F. Rodrigues (Eds.), Mathematics and Music (pp. 65–78). New York, NY: Springer.
Donahue, T. (2005). A guide to musical temperament. Lanham, MD.: Scarecrow Press.
Dourish, P. (2001). Where the action is: The foundations of embodied interaction. Cambridge, MA: MIT Press.
Dreyfus, H. L. (1972). What computers can’t do: A critique of artificial reason (1st ed.). New York, NY: Harper & Row.
Dreyfus, H. L. (1992). What computers still can’t do. Cambridge, MA: MIT Press.
Duch, W., & Jankowski, N. (1999). Survey of neural transfer functions. Neural Computing Surveys, 2, 163–212.
Dutton, J. M., & Starbuck, W. H. (1971). Computer simulation of human behavior. New York, NY: John Wiley & Sons.
Ede, A., & Cormack, L. B. (2004). A history of science in society: From philosophy to utility. Peterborough, ON: Broadview Press.
Einstein, A. (1947). Music in the Romantic era. New York: W. W. Norton.
Elman, J. (1990). Finding structure in time. Cognitive Science, 14, 179–211.
Enquist, M., & Ghirlanda, S. (2005). Neural networks and animal behavior. Princeton, NJ: Princeton University Press.
Erhan, D., Courville, A., & Bengio, Y. (2010). Understanding representations learned in deep architectures (Technical Report 1355). Departement d’Informatique et Recherche Operationnelle, Université de Montréal.
Farrell, J. E., & Shepard, R. N. (1981). Shape, orientation, and apparent rotational motion. Journal of Experimental Psychology: Human Perception and Performance, 7, 477–486.
Fechner, G. T. (1966/1860). Elements of psychophysics (H. E. Adler, Trans., D. H. Howes & E. G. Boring Eds.). New York, NY: Holt.
Feigenbaum, E. A., & Feldman, J. (1995). Computers and thought. Cambridge, MA: MIT Press.
Fiske, H. E. (2004). Connectionist models of musical thinking. Lewiston, NY: E. Mellen Press.
Fletcher, H. (1924). The physical criterion for determining the pitch of a musical tone. Physical Review, 23(3), 427–437.
Fodor, J. A. (1975). The language of thought. Cambridge, MA: Harvard University Press.
Forte, A. (1973). The structure of atonal music. New Haven, CT: Yale University Press.
Forte, A. (1985). Pitch-class set analysis today. Music Analysis, 4(1–2), 29–58. doi: 10.2307/854234
Francès, R. (1988). The perception of music. Hillsdale, N.J.: L. Erlbaum.
Frankland, B. W., & Cohen, A. J. (1996). Using the Krumhansl and Schmuckler key-finding algorithm to quantify the effects of tonality in the interpolated-tone pitch-comparison task. Music Perception, 14(1), 57–83.
Franklin, J. A. (2004). Recurrent neural networks and pitch representations for music tasks. Paper presented at the Seventeenth International Florida Artificial Intelligence Research Symposium Conference, Miami Beach, Florida.
Franklin, J. A. (2006). Jazz melody generation using recurrent networks and reinforcement learning. International Journal on Artificial Intelligence Tools, 15(4), 623–650.
Gaines, J. R. (2005). Evening in the palace of reason: Bach meets Frederick the Great in the Age of Enlightenment. New York, NY: Fourth Estate.
Gallant, S. I. (1993). Neural network learning and expert systems. Cambridge, MA: MIT Press.
Gasser, M., Eck, D., & Port, R. (1999). Meter as mechanism: A neural network model that learns metrical patterns. Connection Science, 11(2), 187–216.
Gjerdingen, R. O. (1990). Categorization of musical patterns by self-organizing neuron-like networks. Music Perception, 7(4), 339–369.
Gjerdingen, R. O. (1992). Learning syntactically significant temporal patterns of chords: A masking field embedded in an ART-3 architecture. Neural Networks, 5(4), 551–564.
Gjerdingen, R. O., & Perrott, D. (2008). Scanning the dial: The rapid recognition of music genres. Journal of New Music Research, 37(2), 93–100. doi: 10.1080/09298210802479268
Gluck, M. A., & Myers, C. (2001). Gateway to memory: An introduction to neural network modeling of the hippocampus and learning. Cambridge, MA: MIT Press.
Graham, R., & Dawson, M. (2005). Using artificial neural networks to examine event-related potentials of face memory. Neural Network World, 15, 215–227.
Griffith, N. (1995). Connectionist visualization of tonal structure. Artificial Intelligence Review, 8(5–6), 393–408.
Griffith, N., & Todd, P. M. (1999). Musical networks: Parallel distributed perception and performance. Cambridge, MA: MIT Press.
Grossberg, S. (1980). How does the brain build a cognitive code? Psychological Review, 87, 1–51.
Grossberg, S. (1987). Competitive learning: From interactive activation to adaptive resonance. Cognitive Science, 11, 23–63.
Grossberg, S. (1988). Neural networks and natural intelligence. Cambridge, MA: MIT Press.
Gu, H., & Lin, Z. (2014). Singing-voice synthesis using ANN vibrator-parameter models. Journal of Information Science and Engineering, 30(2), 425–442.
Guernsey, M. (1928). The role of consonance and dissonance in music. American Journal of Psychology, 40, 173–204. doi: 10.2307/1414484
Guo, J. J., & Luh, P. B. (2004). Improving market clearing price prediction by using a committee machine of neural networks. IEEE Transactions on Power Systems, 19(4), 1867–1876. doi: 10.1109/tpwrs.2004.837759
Handelman, E. J., & Sigler, A. (2013). Key induction and key mapping using pitch-class set assertions. In J. Yust, J. Wild, & J. A. Burgoyne (Eds.), Mathematics and computation in music (pp. 115–127). New York, NY: Springer.
Hanslick, E. (1854/1957). The beautiful in music. New York, NY: Liberal Arts Press.
Hanson, H. (1960). Harmonic materials of modern music. New York, NY: Appleton-Century-Crofts.
Hanson, S. J., & Burr, D. J. (1990). What connectionist models learn: Learning and representation in connectionist networks. Behavioral and Brain Sciences, 13, 471–518.
Hayashi, Y., Setiono, R., & Yoshida, K. (2000). A comparison between two neural network rule extraction techniques for the diagnosis of hepatobiliary disorders. Artificial Intelligence in Medicine, 20(3), 205–216. doi: 10.1016/s0933-3657(00)00064-6
Heidegger, M. (1927/1962). Being and time (J. Macquarrie and E. Robinson, Trans.). New York, NY: Harper & Row.
Helmholtz, H., & Ellis, A. J. (1863/1954). On the sensations of tone as a physiological basis for the theory of music (2nd English ed.). New York, NY: Dover Publications.
Hiebert, E. (2014). The Helmholtz legacy in physiological acoustics. London, UK: Springer.
Hinton, G. E. (1986). Learning distributed representations of concepts. Paper presented at the 8th Annual Meeting of the Cognitive Science Society, Ann Arbor, MI.
Hinton, G. E. (2007). Learning multiple layers of representation. Trends in Cognitive Sciences, 11(10), 428–434. doi: 10.1016/j.tics.2007.09.004
Hinton, G. E., McClelland, J., & Rumelhart, D. (1986). Distributed representations. In D. Rumelhart & J. McClelland (Eds.), Parallel distributed processing (Vol. 1, pp. 77–109). Cambridge, MA: MIT Press.
Hinton, G. E., Osindero, S., & Teh, Y. (2006). A fast learning algorithm for deep belief nets. Neural Computation, 18(7), 1527–1554. doi: 10.1162/neco.2006.18.7.1527
Hinton, G. E., & Salakhutdinov, R. R. (2006). Reducing the dimensionality of data with neural networks. Science, 313(5786), 504–507. doi: 10.1126/science.1127647
Hoeschele, M., Weisman, R. G., Guillette, L. M., Hahn, A. H., & Sturdy, C. B. (2013). Chickadees fail standardized operant tests for octave equivalence. Animal Cognition, 16(4), 599–609. doi: 10.1007/s10071-013-0597-z
Hofstadter, D. R. (1979). Godel, Escher, Bach: An eternal golden braid. New York, NY: Basic Books.
Holtzman, S. R. (1977). Program for key determination. Interface: Journal of New Music Research, 6(1), 29–56.
Hook, J. L. (2006). Exploring musical space. Science, 313(5783), 49–50. doi: 10.1126/science.1129300
Hoover, A. K., & Stanley, K. O. (2009). Exploiting functional relationships in musical composition. Connection Science, 21(2–3), 227–251. doi: 10.1080/09540090902733871
Horgan, T., & Tienson, J. (1996). Connectionism and the philosophy of psychology. Cambridge, MA: MIT Press.
Houston, S. (2004). Play piano in a flash! New York: Hyperion.
Howell, P., Cross, I., & West, R. (1985). Musical structure and cognition. London, UK: Orlando Academic Press.
Hubel, D. H., & Wiesel, T. N. (1959). Receptive fields of single neurones in the cat’s striate cortex. Journal of Physiology, 148, 574–591.
Hui, A. (2013). The psychophysical ear: Musical experiments, experimental sounds, 1840–1910. Cambridge, MA: MIT Press.
Humphrey, E. J., Bello, J. P., & LeCun, Y. (2013). Feature learning and deep architectures: New directions for music informatics. Journal of Intelligent Information Systems, 41(3), 461–481. doi: 10.1007/s10844-013-0248-5
Huron, D. (1999). Music research using Humdrum: A user’s guide. Stanford, CA: Center for Computer Assisted Research in the Humanities.
Huron, D. B. (2006). Sweet anticipation: Music and the psychology of expectation. Cambridge, MA: MIT Press.
Isacoff, S. (2001). Temperament: The idea that solved music’s greatest riddle. New York, NY: Alfred A. Knopf.
Isacoff, S. (2011). A natural history of the piano. New York, NY: Alfred A. Knopf.
Jun, S., Rho, S., & Hwang, E. (2010). Music retrieval and recommendation scheme based on varying mood sequences. International Journal on Semantic Web and Information Systems, 6(2), 1–16. doi: 10.4018/jswis.2010040101
Katz, B. F. (1995). Harmonic resolution, neural resonance, and positive affect. Music Perception, 13(1), 79–108.
Koelle, D. (2008). The complete guide to JFugue: Programming music in Java. www.jfugue.org.
Kohonen, T. (1977). Associative memory: A system-theoretical approach. New York, NY: Springer-Verlag.
Kohonen, T. (1984). Self-organization and associative memory. New York, NY: Springer-Verlag.
Kohonen, T. (2001). Self-organizing maps (3rd ed.). New York, NY: Springer.
Kohonen, T., Laine, P., Tiits, K., & Torkkola, K. (1991). A nonheuristic automatic composing method. In P. M. Todd & D. G. Loy (Eds.), Music and connectionism (pp. 229–242). Cambridge, MA: MIT Press.
Kosslyn, S. M. (1980). Image and mind. Cambridge, MA: Harvard University Press.
Kosslyn, S. M. (1994). Image and brain. Cambridge, MA: MIT Press.
Krumhansl, C. L. (1979). Psychological representation of musical pitch in a tonal context. Cognitive Psychology, 11(3), 346–374. doi: 10.1016/0010-0285(79)90016-1
Krumhansl, C. L. (1990a). Cognitive foundations of musical pitch. New York, NY: Oxford University Press.
Krumhansl, C. L. (1990b). Tonal hierarchies and rare intervals in music cognition. Music Perception, 7(3), 309–324.
Krumhansl, C. L. (2005). The geometry of musical structure: A brief introduction and history. ACM Computers In Entertainment, 3(4), 1–14.
Krumhansl, C. L., Bharucha, J. J., & Kessler, E. J. (1982). Perceived harmonic structure of chords in three related musical keys. Journal of Experimental Psychology: Human Perception and Performance, 8(1), 24–36.
Krumhansl, C. L., & Kessler, E. J. (1982). Tracing the dynamic changes in perceived tonal organization in a spatial representation of musical keys. Psychological Review, 89(4), 334–368.
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.
Kruschke, J. K. (2011). Doing Bayesian data analysis: A tutorial with R and BUGS. Burlington, MA: Academic Press.
Laden, B., & Keefe, B. H. (1989). The representation of pitch in a neural net model of pitch classification. Computer Music Journal, 13, 12–26.
Laitz, S. G. (2008). The complete musician: An integrated approach to tonal theory, analysis, and listening (2nd ed.). New York, NY: Oxford University Press.
Large, E. W., & Kolen, J. F. (1994). Resonance and the perception of musical meter. Connection Science, 6, 177–208.
Larochelle, H., Mandel, M., Pascanu, R., & Bengio, Y. (2012). Learning algorithms for the classification restricted Boltzmann machine. Journal of Machine Learning Research, 13, 643–669.
Larson, W. S. (1930). Measurement of musical talent for the prediction of success in instrumental music. Psychological Monographs, 40(1), 33–73.
Leahey, T. H. (1987). A history of psychology (2nd ed.). Englewood Cliffs, NJ: Prentice-Hall.
Leighton, J. P., & Dawson, M. R. W. (2001). A parallel distributed processing model of Wason’s selection task. Cognitive Systems Research, 2, 207–231.
Leman, M. (1991). The ontogenesis of tonal semantics: Results of a computer study. In P. M. Todd & D. G. Loy (Eds.), Music and connectionism (pp. 100–127). Cambridge, MA: MIT Press.
Lerdahl, F. (2001). Tonal pitch space. New York, NY: Oxford University Press.
Lerdahl, F., & Jackendoff, R. (1983). A generative theory of tonal music. Cambridge, MA: MIT Press.
Lettvin, J. Y., Maturana, H. R., McCulloch, W. S., & Pitts, W. H. (1959). What the frog’s eye tells the frog’s brain. Proceedings of the IRE, 47(11), 1940–1951.
Levine, M. (1989). The jazz piano book. Petaluma, CA: Sher Music Co.
Lewandowsky, S. (1993). The rewards and hazards of computer simulations. Psychological Science, 4, 236–243.
Lewin, D. (2007). Generalized musical intervals and transformations. New York, NY: Oxford University Press.
Lewis, J. P. (1991). Creation by refinement and the problem of algorithmic music composition. In P. M. Todd & D. G. Loy (Eds.), Music and connectionism (pp. 212–228). Cambridge, MA: MIT Press.
Lindsay, P. H., & Norman, D. A. (1972). Human information processing. New York, NY: Academic Press.
Lippmann, R. P. (1989, November). Pattern classification using neural networks. IEEE Communications Magazine, 47–64.
Liu, N. H., Hsieh, S. J., & Tsai, C. F. (2010). An intelligent music playlist generator based on the time parameter with artificial neural networks. Expert Systems with Applications, 37(4), 2815–2825. doi: 10.1016/j.eswa.2009.09.009
Longuet-Higgins, H. C., & Steedman, M. J. (1971). On interpreting Bach. In B. Meltzer & D. Michie (Eds.), Machine intelligence (Vol. 6, pp. 221–239). Edinburgh, UK: Edinburgh University Press.
Longyear, R. M. (1988). Nineteenth-century Romanticism in music (3rd ed.). Englewood Cliffs, N.J.: Prentice Hall.
Loy, D. G. (1991). Connectionism and musiconomy. In P. M. Todd & D. G. Loy (Eds.), Music and connectionism (pp. 20–36). Cambridge, MA: MIT Press.
Lunneborg, C. E. (1994). Modeling experimental and observational data. Belmont, CA: Duxbury Press.
Malmberg, C. F. (1918). The perception of consonance and dissonance. Psychological Monographs, 25(2), 93–133.
Mammone, R. J. (1993). Artificial neural networks for speech and vision. New York, NY: Chapman & Hall.
Marolt, M. (2004a). A connectionist approach to automatic transcription of polyphonic piano music. IEEE Transactions on Multimedia, 6(3), 439–449. doi: 10.1109/tmm.2004.827507
Marolt, M. (2004b). Networks of adaptive oscillators for partial tracking and transcription of music recordings. Journal of New Music Research, 33(1), 49–59. doi: 10.1076/jnmr.33.1.49.35391
Marr, D. (1982). Vision. San Francisco, CA: W.H. Freeman.
Martineau, J. (2008). The elements of music. New York, NY: Walker & Company.
Marwala, T. (2000). Damage identification using committee of neural networks. Journal of Engineering Mechanics-Asce, 126(1), 43–50. doi: 10.1061/(asce)0733-9399(2000)126:1(43)
McClelland, J. (1998). Connectionist models and Bayesian inference. In M. Oaksford & N. Chater (Eds.), Rational models of cognition (pp. 21–53). Oxford, UK: Oxford University Press.
McClelland, J. L., & Rumelhart, D. E. (1986). Parallel distributed processing (Vol. 2). Cambridge, MA: MIT Press.
McCloskey, M. (1991). Networks and theories: The place of connectionism in cognitive science. Psychological Science, 2, 387–395.
McDermott, J., & Hauser, M. (2004). Are consonant intervals music to their ears? Spontaneous acoustic preferences in a nonhuman primate. Cognition, 94(2), B11–B21. doi: 10.1016/j.cognition.2004.04.004
McLachlan, N., Marco, D., Light, M., & Wilson, S. A. (2013). Consonance and pitch. Journal of Experimental Psychology-General, 142(4), 1142–1158. doi: 10.1037/a0030830
Medler, D. A., & Dawson, M. R. W. (1994). Training redundant artificial neural networks: Imposing biology on technology. Psychological Research, 57, 54–62.
Medler, D. A., Dawson, M. R. W., & Kingstone, A. (2005). Functional localization and double dissociations: The relationship between internal structure and behavior. Brain and Cognition, 57, 146–150.
Miller, G. A. (2003). The cognitive revolution: A historical perspective. Trends in Cognitive Sciences, 7(3), 141–144.
Minsky, M. L., & Papert, S. (1969). Perceptrons: An introduction to computational geometry. Cambridge, MA: MIT Press.
Mohamed, A., Dahl, G. E., & Hinton, G. E. (2012). Acoustic modeling using deep belief networks. IEEE Transactions on Audio Speech and Language Processing, 20(1), 14–22. doi: 10.1109/tasl.2011.2109382
Monterola, C., Abundo, C., Tugaff, J., & Venturina, L.E. (2009). Prediction of potential hit song and musical genre using artificial neural networks. International Journal of Modern Physics C, 20(11), 1697–1718. doi: 10.1142/s0129183109014680
Moorhead, I. R., Haig, N. D., & Clement, R. A. (1989). An investigation of trained neural networks from a neurophysiological perspective. Perception, 18, 793–803.
Mostafa, M. M., & Billor, N. (2009). Recognition of Western style musical genres using machine learning techniques. Expert Systems with Applications, 36(8), 11378–11389. doi: 10.1016/j.eswa.2009.03.050
Mozer, M.C. (1991). Connectionist music composition based on melodic, stylistic, and psychophysical constraints. In P.M. Todd & D.G. Loy (Eds.), Music and connectionism (pp. 195–211). Cambridge, MA: MIT Press.
Mozer, M.C. (1994). Neural network music composition by prediction: Exploring the benefits of psychoacoustic constraints and multi-scale processing. Connection Science, 6, 247–280.
Mozer, M.C., & Smolensky, P. (1989). Using relevance to reduce network size automatically. Connection Science, 1, 3–16.
Munoz-Exposito, J. E., Garcia-Galan, S., Ruiz-Reyes, N., & Vera-Candeas, P. (2007). Adaptive network-based fuzzy inference system vs. other classification algorithms for warped LPC-based speech/music discrimination. Engineering Applications of Artificial Intelligence, 20(6), 783–793. doi: 10.1016/j.engappai.2006.10.007
Nagashima, T., & Kawashima, J. (1997). Experimental study on arranging music by chaotic neural network. International Journal of Intelligent Systems, 12(4), 323–339.
Neisser, U. (1967). Cognitive psychology. New York, NY: Appleton-Century-Crofts.
Newell, A. (1980). Physical symbol systems. Cognitive Science, 4, 135–183.
Newell, A., & Simon, H. A. (1961). Computer simulation of human thinking. Science, 134(349), 2011–2017.
Newell, A., & Simon, H. A. (1972). Human problem solving. Englewood Cliffs, NJ: Prentice-Hall.
Nickerson, C. M., Bloomfield, L. L., Dawson, M. R. W., Charrier, I., & Sturdy, C. B. (2007). Feature weighting in “chick-a-dee” call notes of Poecile atricapillus. Journal of the Acoustical Society of America, 122(4), 2451–2458. doi: 10.1121/1.2770540
Nisenson, E. (2000). The making of Kind of Blue: Miles Davis and his masterpiece. New York, NY: St. Martin’s Press.
Norman, D. A. (1998). The invisible computer. Cambridge, MA: MIT Press.
Norman, D. A. (2002). The design of everyday things. New York: Basic Books.
Norman, D. A. (2004). Emotional design: Why we love (or hate) everyday things. New York, NY: Basic Books.
Oaksford, M., & Chater, N. (1991). Against logicist cognitive science. Mind & Language, 6, 1–38.
Oaksford, M., & Chater, N. (2007). Bayesian rationality: The probabilistic approach to human reasoning. New York, NY: Oxford University Press.
Omlin, C. W., & Giles, C. L. (1996). Extraction of rules from discrete-time recurrent neural networks. Neural networks, 9, 41–52.
Page, M. P. A. (1994). Modeling the perception of musical sequences with self-organizing neural networks. Connection Science, 6, 223–246.
Pao, Y. -H. (1989). Adaptive pattern recognition and neural networks. Reading, MA: Addison-Wesley.
Patel, A. D. (2008). Music, language, and the brain. New York, NY: Oxford University Press.
Pesic, P. (2010). Hearing the irrational: Music and the development of the modern concept of number. Isis, 101(3), 501–530.
Plantinga, J., & Trehub, S. E. (2014). Revisiting the innate preference for consonance. Journal of Experimental Psychology-Human Perception and Performance, 40(1), 40–49. doi: 10.1037/a0033471
Plantinga, L. (1984). Romantic music: A history of musical style in nineteenth-century Europe. New York, NY: W.W. Norton.
Plomp, R., & Levelt, W. J. M. (1965). Tonal consonance and critical bandwidth. Journal of the Acoustical Society of America, 38(4), 548–560.
Pollack, J. B. (1990). Recursive distributed representations. Artificial Intelligence, 46, 77–105.
Porter, L. (1998). John Coltrane: His life and music. Ann Arbor: University of Michigan Press.
Pylyshyn, Z. W. (1980). Computation and cognition: Issues in the foundations of cognitive science. Behavioral and Brain sciences, 3(1), 111–132.
Pylyshyn, Z. W. (1984). Computation and cognition. Cambridge, MA: MIT Press.
Ramsey, W., Stich, S. P., & Rumelhart, D. E. (1991). Philosophy and connectionist theory. Hillsdale, NJ: Lawrence Erlbaum Associates.
Restle, F. (1971). Mathematical models in psychology: An introduction. Baltimore, MD: Penguin.
Révész, G. (1954). Introduction to the psychology of music. Norman: University of Oklahoma Press.
Reynolds, A. G., & Flagg, P. W. (1977). Cognitive psychology. Cambridge, MA: Winthrop.
Ripley, B. D. (1996). Pattern recognition and neural networks. Cambridge, UK: Cambridge University Press.
Roig-Francolí, M. A. (2008). Understanding post-tonal music. Boston: McGraw-Hill.
Rojas, R. (1996). Neural networks: A systematic exploration. Berlin: Springer.
Rosch, E. (1975). Cognitive reference points. Cognitive Psychology, 7(4), 532–547.
Rosch, E., & Mervis, C. B. (1975). Family resemblances: Studies in the internal structure of categories. Cognitive Psychology, 7, 573–605.
Rosenblatt, F. (1958). The perceptron: A probabilistic model for information storage and organization in the brain. Psychological Review, 65(6), 386–408.
Rosenblatt, F. (1962). Principles of neurodynamics. Washington, DC: Spartan Books.
Rowe, R. (2001). Machine musicianship. Cambridge, MA: MIT Press.
Rumelhart, D. E., Hinton, G. E., & Williams, R. J. (1986). Learning representations by back-propagating errors. Nature, 323, 533–536.
Rumelhart, D. E., & McClelland, J. (1986a). On learning the past tenses of English verbs. In J. McClelland & D. E. Rumelhart (Eds.), Parallel distributed processing. Volume 2: Psychological and biological models (pp. 216–271). Cambridge, MA: MIT Press.
Rumelhart, D. E., & McClelland, J. (1986b). Parallel distributed processing (Vol. 1). Cambridge, MA: MIT Press.
Sano, H., & Jenkins, B. K. (1989). A neural network model for pitch perception. Computer Music Journal, 13(3), 41–48. doi: 10.2307/3680010
Sapp, C. S. (2005). Visual hierarchical key analysis. ACM Computers in Entertainment, 3(4), 1–19.
Sarikaya, R., Hinton, G. E., & Deoras, A. (2014). Application of deep belief networks for natural language understanding. IEEE-ACM Transactions on Audio Speech and Language Processing, 22(4), 778–784. doi: 10.1109/taslp.2014.2303296
Scarborough, D. L., Miller, B. O., & Jones, J. A. (1989). Connectionist models for tonal analysis. Computer Music Journal, 13(3), 49–55.
Schmajuk, N. A. (1997). Animal learning and cognition: A neural network approach. New York, NY: Cambridge University Press.
Schmuckler, M. A., & Tomovski, R. (2005). Perceptual tests of an algorithm for musical key-finding. Journal of Experimental Psychology: Human Perception and Performance, 31(5), 1124–1149. doi: 10.1037/0096-1523.31.5.1124
Schoenberg, A. (1969). Structural functions of harmony (Rev. ed.). New York, NY: W. W. Norton.
Schumacher, M., Rossner, R., & Vach, W. (1996). Neural networks and logistic regression. Computational Statistics & Data Analysis, 21(6), 661–682. doi: 10.1016/0167-9473(95)00032-1
Schwab, E.C., & Nusbaum, H.C. (1986). Pattern recognition by humans and machines: Visual perception (Vol. 2). Orlando, FL: Academic Press.
Scimemi, B. (2002). The use of mechanical devices and numerical algorithms in the 18th century for the equal temperament of the musical scale. In G. Assayag, H. G. Feichtinger & J.-F. Rodrigues (Eds.), Mathematics and music (pp. 49–64). New York: Springer.
Searle, J. R. (1984). Minds, brains and science. Cambridge, MA: Harvard University Press.
Seashore, C. E. (1915). The measurement of musical talent. The Musical Quarterly, 1(1), 129–148.
Seashore, C. E. (1936). Objective analysis of musical performance. Iowa City, IA: The University Press.
Seashore, C. E. (1938/1967). Psychology of music. New York: Dover.
Seidenberg, M. (1993). Connectionist models and cognitive theory. Psychological Science, 4, 228–235.
Serafine, M. L. (1988). Music as cognition: The development of thought in sound. New York, NY: Columbia University Press.
Setiono, R., Baesens, B., & Mues, C. (2011). Rule extraction from minimal neural networks for credit card screening. International Journal of Neural Systems, 21(4), 265–276. doi: 10.1142/s0129065711002821
Setiono, R., Thong, J. Y. L., & Yap, C. S. (1998). Symbolic rule extraction from neural networks: An application to identifying organizations adopting IT. Information & Management, 34(2), 91–101. doi: 10.1016/s0378-7206(98)00048-2
Shanahan, D., & Broze, Y. (2012). A diachronic analysis of harmonic schemata in jazz. Paper presented at the 12th International Conference on Music Perception and Cognition, Thessaloniki, Greece.
Shapin, S. (1996). The Scientific Revolution. Chicago, IL: University of Chicago Press.
Shepard, R. N. (1964). Circularity in judgments of relative pitch. Journal of the Acoustical Society of America, 36(12), 2346–53. doi: 10.1121/1.1919362
Shepard, R. N. (1984). Ecological constraints on internal representation: Resonant kinematics of perceiving, imagining, thinking, and dreaming. Psychological Review, 91, 417–447.
Shepard, R. N. (1990). Mind sights: Original visual illusions, ambiguities, and other anomalies. New York, NY: W.H. Freeman and Co.
Shepherd, A. J. (1997). Second-order methods for neural networks. London, UK: Springer.
Shibata, N. (1991). A neural network-based method for chord note scale association with melodies. Nec Research & Development, 32(3), 453–459.
Shmulevich, I., & Yli-Harja, O. (2000). Localized key finding: Algorithms and applications. Music Perception, 17(4), 531–544.
Siegelmann, H. T. (1999). Neural networks and analog computation: Beyond the Turing limit. Boston, MA: Birkhauser.
Siegelmann, H. T., & Sontag, E. D. (1991). Turing computability with neural nets. Applied Mathematics Letters, 4, 77–80.
Simon, H. A. (1969). The sciences of the artificial. Cambridge, MA: MIT Press.
Sloboda, J. A. (1985). The musical mind: The cognitive psychology of music. Oxford, UK: Oxford University Press.
Smolensky, P. (1988). On the proper treatment of connectionism. Behavioral and Brain Sciences, 11, 1–74.
Smolensky, P., & Legendre, G. (2006). The harmonic mind: From neural computation to optimality-theoretic grammar. Cambridge, MA: MIT Press.
Snyder, B. (2000). Music and memory: An introduction. Cambridge, MA: MIT Press.
Sperry, R. W. (1993). The impact and promise of the cognitive revolution. American Psychologist, 48(8), 878–885.
Stanton, H. M., & Seashore, C. E. (1935). Measurement of musical talent: The Eastman experiment. Iowa City, IA: The University Press.
Stephenson, B. (1994). The music of the heavens: Kepler’s harmonic astronomy. Princeton, NJ: Princeton University Press.
Stevens, C., & Latimer, C. (1992). A comparison of connectionist models of music recognition and human performance. Minds and Machines, 2(4), 379–400.
Straus, J. N. (1991). A primer for atonal set theory. College Music Symposium, 31(1–26).
Straus, J. N. (2005). Introduction to post-tonal theory (3rd ed.). Upper Saddle River, NJ: Prentice Hall.
Sudnow, D. (1978). Ways of the hand: The organization of improvised conduct. Cambridge, MA: Harvard University Press.
Sullivan, J. W. N. (1927). Beethoven: His spiritual development. London, UK: J. Cape.
Taha, I. A., & Ghosh, J. (1999). Symbolic interpretation of artificial neural networks. IEEE Transactions on Knowledge and Data Engineering, 11(3), 448–463. doi: 10.1109/69.774103
Takane, Y., Oshima-Takane, Y., & Shultz, T. R. (1994). Approximations of nonlinear functions by feed-forward neural networks Proceedings of the Japan Classification Society Meeting (pp. 26–33). Tokyo: The Japan Classification Society.
Temperley, D. (1999). What’s key for key? The Krumhansl-Schmuckler key-finding algorithm reconsidered. Music Perception, 17(1), 65–100.
Temperley, D. (2001). The cognition of basic musical structures. Cambridge, MA: MIT Press.
Temperley, D. (2004). Bayesian models of musical structure and cognition. Musicae Scientiae, 8(2), 175–205.
Temperley, D. (2007). Music and probability. Cambridge, MA: MIT Press.
Temperley, D., & Marvin, E. W. (2008). Pitch-class distribution and the identification of key. Music Perception, 25(3), 193–212. doi: 10.1525/mp.2008.25.3.193
Terhardt, E., Stoll, G., & Seewann, M. (1982a). Algorithm for extraction of pitch and pitch salience from complex tonal signals. Journal of the Acoustical Society of America, 71(3), 679–688.
Terhardt, E., Stoll, G., & Seewann, M. (1982b). Pitch of complex signals according to virtual-pitch theory: Tests, examples, and predictions. Journal of the Acoustical Society of America, 71(3), 671–678.
Thomas, J. C. (1975). Chasin’ the trane: The music and mystique of John Coltrane (1st ed.). Garden City, N.Y.: Doubleday.
Thrun, S. (1995). Extracting rules from artificial neural networks with distributed representations. In G. Tesauro, D. S. Touretzky & T. K. Leen (Eds.), Advances in neural information processing systems (Vol. 7, pp. 505–512). Cambridge, MA: MIT Press.
Todd, P. M. (1989). A connectionist approach to algorithmic composition. Computer Music Journal, 13(4), 27–43.
Todd, P. M., & Loy, D. G. (1991). Music and connectionism. Cambridge, MA: MIT Press.
Tymoczko, D. (2006). The geometry of musical chords. Science, 313(5783), 72–74.
Tymoczko, D. (2008). Scale theory, serial theory and voice leading. Music Analysis, 27(1), 1–49. doi: 10.1111/j.1468-2249.2008.00257.x
Tymoczko, D. (2011). A geometry of music: Harmony and counterpoint in the extended common practice (E-pub ed.). New York, NY: Oxford University Press.
Tymoczko, D. (2012). The generalized Tonnetz. Journal of Music Theory, 56(1), 1–52. doi: 10.1215/00222909-1546958
Van Egmond, R., & Butler, D. (1997). Diatonic connotations of pitch-class sets. Music Perception, 15(1), 1–29.
Van Gelder, T. (1991). What is the “D” in “PDP”? A survey of the concept of distribution. In W. Ramsey, S. P. Stich & D. E. Rumelhart (Eds.), Philosophy and connectionist theory (pp. 33–59). Hillsdale, NJ: Lawrence Erlbaum Associates.
Vos, P. G., & Troost, J. M. (1989). Ascending and descending melodic intervals: Statistical findings and their perceptual relevance. Music Perception, 6(4), 383–396.
Vos, P. G., & Van Geenen, E. W. (1996). A parallel-processing key-finding model. Music Perception, 14(2), 185–223.
Watson, J. B. (1913). Psychology as the behaviorist views it. Psychological Review, 20, 158–177.
Waugh, N. C., & Norman, D. A. (1965). Primary memory. Psychological Review, 72, 89–104.
Wechsler, H. (1992). Neural networks for perception: Computation, learning, and architectures (Vol. 2). Boston, MA: Academic Press.
Whittall, A. (1987). Romantic music: A concise history from Schubert to Sibelius. London, UK: Thames and Hudson.
Wieczorkowska, A. A., & Kubera, E. (2010). Identification of a dominating instrument in polytimbral same-pitch mixes using SVM classifiers with non-linear kernel. Journal of Intelligent Information Systems, 34(3), 275–303. doi: 10.1007/s10844-009-0098-3
Winston, P. H. (1975). The psychology of computer vision. New York, NY: McGraw-Hill.
Wood, G. (2002). Living dolls: A magical history of the quest for artificial life. London, UK: Faber and Faber.
Yaremchuk, V., & Dawson, M. R. W. (2005). Chord classifications by artificial neural networks revisited: Internal representations of circles of major thirds and minor thirds. Artificial Neural Networks: Biological Inspirations – Icann 2005, Pt. 1, Proceedings, 3696, 605–610.
Yaremchuk, V., & Dawson, M. R. W. (2008). Artificial neural networks that classify musical chords. International Journal of Cognitive Informatics and Natural Intelligence, 2(3), 22–30.
Zhao, Z. Q., Huang, D. S., & Sun, B. Y. (2004). Human face recognition based on multi-features using neural networks committee. Pattern Recognition Letters, 25(12), 1351–1358. doi: 10.1016/j.patrec.2004.05.008
We use cookies to analyze our traffic. Please decide if you are willing to accept cookies from our website. You can change this setting anytime in Privacy Settings.