Music, mind and mathematics: theory, reality and formality

Wiggins, Geraínt A.

doi:10.1080/17459737.2012.694710

“…In this context, many structures identified in music theory provide useful formalizations of relevant cognitive structures (Jackendoff and Lerdahl, 2006;Wiggins, 2012b;Koelsch, 2012;Rohrmeier and Pearce, 2018;Cecchetti et al, 2020). As such, they may inform computational modeling about relevant kinds of latent concepts, relations and dependencies to take into account.…”

Section: The Cognitive Challengementioning

confidence: 99%

On Creativity, Music’s AI Completeness, and Four Challenges for Artificial Musical Creativity

Rohrmeier

¹

2022

Transactions of the International Society for Music Information Retrieval

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This article explores the notion of human and computational creativity as well as core challenges for computational musical creativity. It also examines the philosophical dilemma of computational creativity as being suspended between algorithmic determinism and random sampling, and suggests a resolution from a perspective that conceives of "creativity" as an essentially functional concept dependent on a problem space, a frame of reference (e.g. a standard strategy, a gatekeeper, another mind, or a community), and relevance. Second, this article proposes four challenges for artificial musical creativity and musical AI: (1) the cognitive challenge that musical creativity requires a model of music cognition, (2) the challenge of the external world, that many cases of musical creativity require references to the external world, (3) the embodiment challenge, that many cases of musical creativity require a model of the human body, the instrument(s) and the performative setting in various ways, (4) the challenge of creativity at the meta-level, that musical creativity across the board requires creativity at the meta-level. Based on these challenges it is argued that the general capacity of music and its creation fundamentally involves general (artificial) intelligence and that therefore musical creativity at large is fundamentally an AI-complete problem.

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“…

Digital Musicology is a vibrant and quickly growing discipline that addresses traditional and novel music-related research questions with digital and computational means (Honing, 2006;Huron, 1999;Urberg, 2017). Research questions and methods often overlap with or draw on those from diverse disciplines such as music theory and analysis, composition history, mathematics, cognitive psychology, linguistics, anthropology, or computer science (Volk & Honingh, 2012;Wiggins, 2012). Corpus research, i.e., the computational study of representative collections of texts (in the case of linguistics) or notated music (in musicology), plays a prominent role in this trans-disciplinary quest to "make sense of music" through scientific models (London, 2013;Moss, 2019;Shanahan, 2022).

…”

mentioning

confidence: 99%

ms3: A parser for MuseScore files, serving as data factory for annotated music corpora

Hentschel¹,

Rohrmeier²

2023

JOSS

3

0

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Digital Musicology is a vibrant and quickly growing discipline that addresses traditional and novel music-related research questions with digital and computational means (Honing, 2006;Huron, 1999;Urberg, 2017). Research questions and methods often overlap with or draw on those from diverse disciplines such as music theory and analysis, composition history, mathematics, cognitive psychology, linguistics, anthropology, or computer science (Volk & Honingh, 2012;Wiggins, 2012). Corpus research, i.e., the computational study of representative collections of texts (in the case of linguistics) or notated music (in musicology), plays a prominent role in this trans-disciplinary quest to "make sense of music" through scientific models (London, 2013;Moss, 2019;Shanahan, 2022). ms3 makes scores (symbolic representations of music) operational for computational approaches by representing their contents as sets of tabular files.

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