Computational Evolutionary Musicology



The beginning of Chapter 2 offered a sensible definition of music as temporally organized sound. In the broader sense of this definition, one could arguably state that music is not uniquely human. A number of other animals also seem to have music of some sort. Complex vocalizations can be found in many birds (Marler and Slabbekoorn 2004), as well as in mammals such as whales (Payne and McVay 1971) and bats (Behr and von Helversen 2004). In a chapter suggestively entitled ‘Zoomusicologie’ in the book Musique, Mythe, Nature ou Les Dauphins d’Arion, Mâche (1991) presents an interesting discussion on the formal sophistication of various birdcalls. Recently Holy and Guo (2005) demonstrated that the ultrasonic vocalizations that male mice produce when they encounter female mice or their pheromones have the characteristics of song. What is intriguing is that primates who are close related to humans are not as ‘musical’ as those mammals that are far more distantly related to us. This intriguing fact suggests that music might have evolved independently among various types of animals, at various degrees of sophistication. In this context, it would be perfectly plausible to suggest the notion that robots might also be able to evolve music.


Motor Control Mirror Neuron Pitch Contour Winning Neuron Categorization Space 
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  1. Behr, O. and von Helversen, O. (2004). Bat serenades—Complex courtship songs of the sac-winged bat “Saccopteryx bilineatta”. Behavioral Ecology and Sociobiology, 56: 106–115.CrossRefGoogle Scholar
  2. Bidlack, R. (1992). Chaotic systems as simple (but complex) compositional algorithms. Computer Music Journal, 16(3): 33–47.CrossRefGoogle Scholar
  3. Boersma, P. (1993). Articulatory Synthesizers for the Simulations of Consonants. Proceedings of Eurospeech'93, Berlin, Germany, pp. 1907–1910.Google Scholar
  4. Brodie, R. (1996). Virus of the Mind: The New Science of the Meme. Integral Press, Walnut Creek, CA.Google Scholar
  5. Brown, S. (2000). The “Musilanguage” model of music evolution. In N.B. Merker and S. Brown (Eds.), The Origins of Music. The MIT Press, Cambridge, USA.Google Scholar
  6. Burton, A.R. and Vladimirova, T. (1997). A Genetic Algorithm Utilising Neural Network Fitness Evaluation for Musical Composition, In G.D. Smith, N.C. Steele and R.F. Albrecht (Eds.), Proceedings of the 1997 International Conference on Artificial Neural Networks and Genetic Algorithms, Springer-Verlag, Vienna, pp. 220–224.Google Scholar
  7. Cangelosi, A. and Parisi, D. (Eds.) (2001). Simulating the Evolution of Language. Springer Verlag, London, UK.Google Scholar
  8. Casti, J.L. (1997). Would-be Worlds: How Simulation of Changing the Frontiers of Science. John Wiley & Sons, NY.Google Scholar
  9. Christiansen, M.H. and Kirby, S. (Eds.) (2003). Language Evolution: The States of the Art. Oxford University Press, Oxford, UK.Google Scholar
  10. Cope, D. (1996). Experiments in Musical Intelligence. Madison, A-R Editions Inc., WI.Google Scholar
  11. Darwin, C. (1992) (1st published in 1871). The Descent of Man and Selection in Relation to Sex. Princeton University Press, Princeton, NJ.Google Scholar
  12. Freeman, W. (1995). Societies of Brains: A Study in the Neuroscience of Love and Hate. Lawrence Erlbaum Associates, Mahwah, NJ.Google Scholar
  13. Gallese, V. and Goldman, A. (1998). Mirror-neurons and the simulation theory of mind-reading. Trends in Cognitive Sciences, 12: 493–501.CrossRefGoogle Scholar
  14. Glaser, R. (2001). Biophysics. Springer, Heidelberg.Google Scholar
  15. Holy, T.E. and Guo, Z. (2005). Ultrasonic Songs of Male Mice. PLoS Biology, 3(12): e386.CrossRefGoogle Scholar
  16. James, D.L. and Miikkulainen, R. (1995). SARDNET: a self-organizing feature map for sequences. In G. Tesauro, D. Touretzky and T. Leen (Eds), Advances in Neural Information Processing Systems 7. MIT Press, Cambridge, MA.Google Scholar
  17. Kohonen, T. (1997). Self-Organizing Maps. Springer Series in Information Sciences. Springer-Verlag, Heidelberg.Google Scholar
  18. Levy, S. (1993). Artificial Life: A Report from the Frontier where Computers meets Biology. Vintage, London, UK.Google Scholar
  19. Locke, J.L. (1993). The Child's Path to Spoken Language. Harvard University Press, Cambridge, MA.Google Scholar
  20. Mâche, F.-B. (1991). Musique, Mythe, Nature ou les Dauphins d'Arion. Méridiens Klincksieck, Paris.Google Scholar
  21. Marler, P. and Slabbekoorn, H. (Eds.) (2004). Nature's music: The science of birdsong. Elsevier, Boston, MA.Google Scholar
  22. Martins, J. and Miranda, E. R. (2006). A Connectionist architecture for the evolution of rhythms. Proceedings of EvoWorkshops 2006, LNCS 3970. Springer, New York, pp. 696–706.Google Scholar
  23. Milicevic, M. (1996). The Impact of Fractals, Chaos and Complexity on Computer Music Composition. Proceedings of International Computer Music Conference (ICMC 96). Hong Kong, International Computer Music Association, San Francisco, pp. 473–476.Google Scholar
  24. Miller, G. (2000). Evolution of human music through sexual selection. In N. Wallin, B. Merker and S. Brown (Eds.), The Origins of Music. The MIT Press, Cambridge, MA, pp. 329–360.Google Scholar
  25. Miranda, E. R. and Drouet, E. (2006). Evolution of musical lexicons by babbling robots. Proceedings of Towards Autonomous and Robotic Systems 2006, University of Surrey, Gilford, UK. On-line proceedings: (Accessed 17 Nov 2006).Google Scholar
  26. Miranda, E.R. (2002b). Mimetic model of intonation. In C. Anagnostopoulou, M. Ferrand and A. Smaill (Eds.), Music and Artificial Intelligence—Second International Conference ICMAI 2002. Lecture Notes on Artificial Intelligence 2445, Springer-Verlag, Berlin, Germany, pp. 107–118.Google Scholar
  27. Miranda, E.R. (2002a). Computer Sound Design: Synthesis Techniques and Programming. Focal Press, Oxford, UK.Google Scholar
  28. Miranda, E.R. (2001). Synthesising prosody with variable resolution. AES Convention Paper 5332. Audio Engineering Society, Inc., NY, USA.Google Scholar
  29. Mithen, S. (2005). The Singing Neanderthal: The Origins of Music, Language, Mind and Body. Weidenfeld & Nicolson, London.Google Scholar
  30. Mozer, M. (1994). Neural network music composition by prediction: Exploring the benefits of psychophysical constraints and multiscale processing. Connection Science, 6: 247–280.CrossRefGoogle Scholar
  31. Näätänen, R., Tervaniemi, M., Sussman, E., Paavilainen, P. and Winkler, I. (2001). Primitive intelligence in the auditory cortex, Trends in Neurosciences, 24: 283–288.CrossRefGoogle Scholar
  32. Nazzi, T., Floccia, C. and Bertoncini, J., (1998). Discrimination of pitch contours by neonates. Infant Behaviour, 12: 543–554.Google Scholar
  33. Papadopoulos, G. and Wiggins, G. (1998). A Genetic Algorithm for the Generation of Jazz Melodies. Proceedings of 8th Finnish Conference on Artificial Intelligence, Jyväskylä, Finland.Google Scholar
  34. Payne, R.S. and McVay, S. (1971). Songs of humpback whales, Science, 173: 585–597.CrossRefGoogle Scholar
  35. Parsons, L.M. (2003). Exploring the Functional Neuroanatomy of Music Performance, Perception and Comprehension, In I. Peretz and R. Zatorre (Eds.), The Cognitive Neuroscience of Music. Oxford University Press, Oxford, UK, pp. 247–268.Google Scholar
  36. Peretz, I. and Coltheart, M. (2003). Modularity of music processing. Nature Neuroscience, 6: 688–691.CrossRefGoogle Scholar
  37. Peretz, I., Kolinsky, R., Tramo, M., Labrecque, L., Hublet, C. and Demeurisse, G. (1994). Functional dissociations following bilateral lesions of auditory cortex. Brain, 117: 1283–1301.CrossRefGoogle Scholar
  38. Premack, D. and Woodruff, G. (1978). Does the chimpanzee have a theory of mind? Behaviour and Brain Sciences, 1(4): 515–526.CrossRefGoogle Scholar
  39. Rousseau, J.-J. (1990) (1st published in 1765). Essay sur l'origine des langues. Gallimard, Paris.Google Scholar
  40. Rizzolatti, G. and Craighero, L. (2004). The mirror-neuron system. Annual Review of Neuroscience, 27: 169–192.CrossRefGoogle Scholar
  41. Salu, Y. (2001). Understanding Brain and Mind: A Connectionist Perspective. World Scientific, Singapore.zbMATHGoogle Scholar
  42. Steedman, M. (1984). A generative grammar for jazz chord sequences. Music Perception, 2: 52–77.Google Scholar
  43. Steels, L. (1997). The Origins of Syntax in Visually Grounded Robotic Agents. Proceedings of International Joint Conference on Artificial Intelligence (IJCAI'97). Nagoya, Aichi, Japan.Google Scholar
  44. Thomas, D.A. (1995). Music and the Origins of Language. Cambridge University Press, Cambridge, UK.Google Scholar
  45. Todd, P.M. and Loy, D.G. (Eds.) (1991). Music and Connectionism. The MIT Press, Cambridge, MA.Google Scholar
  46. Todd, P.M. and Werner, G.M. (1999). Frankensteinian Methods for Evolutionary Music Composition. In N. Griffith and P.M. Todd (Eds.), Musical Networks: Parallel Distributed Perception and Performance. The MIT Press/Bradford Books, Cambridge, USA, pp. 313–339.Google Scholar
  47. Wallin, N.J., Merker, B. and Brown, S. (Eds.) (2000). The Origins of Music. The MIT Press, Cambridge, USA.Google Scholar
  48. Wray, A. (1998). Protolanguage as a holistic system for social interaction. Language and Communication, 18: 46–667.CrossRefGoogle Scholar
  49. Zinkovsky, A.V., Sholuha, V.A. and Ivanov, A.A. (1996). Mathematical Modelling and Computing Simulation of Biomechanical Systems. World Scientific, Singapore.Google Scholar

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