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    Composing music with bio-technology: an intelligent algorithmic composition system using Physarum polycephalum-based memristors

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    Authors
    Venkatesh, Satvik
    Braund, Edward
    Miranda, Eduardo Reck
    Issue Date
    2022-10-01
    Subjects
    music
    biotechnology
    music composition
    
    Metadata
    Show full item record
    Other Titles
    Unconventional Computing, Arts, Philosophy
    Abstract
    Computer-assisted and automated composition systems often harness Artificial Intelligence models such as Markov chains, Neural Networks, and Genetic Algorithms to generate musical material. The field of Unconventional Computing (UC) explores non-digital ways of data storage, processing, input, and output. UC paradigms such as Biocomputing and Quantum Computing delve into domains beyond the binary bit to handle complex nonlinear functions. In this chapter, we explore harnessing the biological computing substrate Physarum polycephalum as a memristor to process and generate musical material within an algorithmic composition system. It details the journey and process of creating a piece of popular music using this novel technology, which we originally showcased at the New Interfaces for Musical Expression (NIME) conference in 2020. In this piece, entitled Creep into my Lawn, the Physarum polycephalum-based memristors act as creative collaborators in the composition process. Our work aims to demonstrate the potential of UC paradigms in creative applications and to disseminate this technology to non-experts and musicians so that they can incorporate it into their creative processes.
    Citation
    Venkatesh S, Braund E, Miranda ER (2022) 'Composing music with bio-technology: an intelligent algorithmic composition system using Physarum polycephalum-based memristors', in Adamatzky A (ed(s).). Unconventional Computing, Arts, Philosophy, World Scientific pp.535-555.
    Publisher
    World Scientific
    URI
    http://hdl.handle.net/10547/626647
    DOI
    10.1142/9789811257155_0035
    Additional Links
    https://www.worldscientific.com/doi/10.1142/9789811257155_0035
    Type
    Book chapter
    Language
    en
    ISBN
    9789811257148
    9789811257162
    9789811257155
    ae974a485f413a2113503eed53cd6c53
    10.1142/9789811257155_0035
    Scopus Count
    Collections
    Computing

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