Beyond Diverse Datasets: Responsible MIR, Interdisciplinarity, and the Fractured Worlds of Music

Huang, Rujing; Holzapfel, Andre; Sturm, Bob L.; Kaila, Anna-Kaisa

doi:10.5334/tismir.141

Cited by 2 publications

(1 citation statement)

References 25 publications

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“…Continued efforts to expand the diversity of participant and musical samples require dedicated, strategic effort, but come with the fundamental benefit of increasing our confidence in music psychological theories and their relevance to humans and music in the broadest sense. Beyond our immediate field, inclusive theories of music psychology have the power to inform and catalyse change in related areas, such as Music Information Retrieval, music-related artificial intelligence, and music education (Bull et al, 2023;Campbell et al, 2005;Huang et al, 2023;Sturm et al, 2019).…”

Section: Discussionmentioning

confidence: 99%

Participant and Musical Diversity in Music Psychology Research

Jakubowski,

Ahmad,

Armitage

et al. 2024

Preprint

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Research on music psychology has increased exponentially over the past half century, providing insights on a wide range of topics underpinning the perception, cognition, and production of music. This wealth of research means we are now in a place to develop broad, testable theories on the psychology of music, with the potential to impact our wider understanding of human biology, culture, and communication. However, the development of widely applicable and inclusive theories of human responses to music requires these theories to be informed by data that is representative of the global human population and its diverse range of music-making practices. The goal of the present paper is to survey the current state of the field of music psychology in terms of the participant samples and musical samples used. We reviewed and coded relevant details from all articles published in Music Perception, Musicae Scientiae, and Psychology of Music between 2010 to 2022. We found that music psychologists show a substantial tendency to collect data from young adults and university students in Western countries in response to Western music, replicating trends seen across psychology research as a whole. Even data collected in non-Western countries tends to come from a similar demographic to studies of Western participants (e.g., university students, young adults). Some positive trends toward increasing participant diversity have been evidenced over the past decade, although there is still much work to be done, and certain subtopics in the field appear to be more prone to these sampling biases than others. Recommendations for future diversification of research in our field are made, with the aim of increasing our confidence in music psychological theories and their relevance to humans and music in the broadest sense.

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Section: Discussionmentioning

confidence: 99%

Participant and Musical Diversity in Music Psychology Research

Jakubowski,

Ahmad,

Armitage

et al. 2024

Preprint

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Artificial Intelligence and Musicking

Berkowitz

2024

Music Perception: An Interdisciplinary Journal

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Artificial intelligence (AI) deployed for customer relationship management (CRM), digital rights management (DRM), content recommendation, and content generation challenge longstanding truths about listening to and making music. CRM uses music to surveil audiences, removes decision-making responsibilities from consumers, and alters relationships among listeners, artists, and music. DRM overprotects copyrighted content by subverting Fair Use Doctrine and privatizing the Public Domain thereby restricting human creativity. Generative AI, often trained on music misappropriated by developers, renders novel music that seemingly represents neither the artistry present in the training data nor the handiwork of the AI’s user. AI music, as such, appears to be produced through AI cognition, resulting in what some have called “machine folk” and contributing to a “culture in code.” A philosophical analysis of these relationships is required to fully understand how AI impacts music, artists, and audiences. Using metasynthesis and grounded theory, this study considers physical reductionism, metaphysical nihilism, existentialism, and modernity to describe the quiddity of AI’s role in the music ecosystem. Concluding thoughts call researchers and educators to act on philosophical and ethical discussions of AI and promote continued research, public education, and democratic/laymen intervention to ensure ethical outcomes in the AI music space.

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Beyond Diverse Datasets: Responsible MIR, Interdisciplinarity, and the Fractured Worlds of Music

Cited by 2 publications

References 25 publications

Participant and Musical Diversity in Music Psychology Research

Participant and Musical Diversity in Music Psychology Research

Artificial Intelligence and Musicking

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