Human, I wrote a song for you: An experiment testing the influence of machines’ attributes on the AI-composed music evaluation

Hong, Joo-Wha; Fischer, Katrin; Ha, Yul; Zeng, Yilei

doi:10.1016/j.chb.2022.107239

Cited by 29 publications

(8 citation statements)

References 33 publications

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“…Moreover, when viewing anthropomorphized videos of robots painting, participants show greater appreciation for computer-generated art than when no anthropomorphized video of the robot is provided. This mirrors findings by Hong et al ( 2022 ), who found that anthropomorphization of an AI-music algorithm led to higher acceptance of the algorithm as a true “musician,” which in turn led to increased aesthetic appreciation. Collectively, these studies suggest that there may be an implicit role of effortfulness or embodiment behind peoples’ evaluations of art.…”

Section: Studysupporting

confidence: 84%

Humans versus AI: whether and why we prefer human-created compared to AI-created artwork

et al. 2023

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With the recent proliferation of advanced artificial intelligence (AI) models capable of mimicking human artworks, AI creations might soon replace products of human creativity, although skeptics argue that this outcome is unlikely. One possible reason this may be unlikely is that, independent of the physical properties of art, we place great value on the imbuement of the human experience in art. An interesting question, then, is whether and why people might prefer human-compared to AI-created artworks. To explore these questions, we manipulated the purported creator of pieces of art by randomly assigning a “Human-created” or “AI-created” label to paintings actually created by AI, and then assessed participants’ judgements of the artworks across four rating criteria (Liking, Beauty, Profundity, and Worth). Study 1 found increased positive judgements for human- compared to AI-labelled art across all criteria. Study 2 aimed to replicate and extend Study 1 with additional ratings (Emotion, Story, Meaningful, Effort, and Time to create) intended to elucidate why people more-positively appraise Human-labelled artworks. The main findings from Study 1 were replicated, with narrativity (Story) and perceived effort behind artworks (Effort) moderating the label effects (“Human-created” vs. “AI-created”), but only for the sensory-level judgements (Liking, Beauty). Positive personal attitudes toward AI moderated label effects for more-communicative judgements (Profundity, Worth). These studies demonstrate that people tend to be negatively biased against AI-created artworks relative to purportedly human-created artwork, and suggest that knowledge of human engagement in the artistic process contributes positively to appraisals of art.

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Section: Studysupporting

confidence: 84%

Humans versus AI: whether and why we prefer human-created compared to AI-created artwork

et al. 2023

View full text Add to dashboard Cite

show abstract

“…Moreover, when viewing anthropomorphized videos of robots painting, participants show greater appreciation for computer-generated art than when no anthropomorphized video of the robot is provided. This mirrors findings by Hong et al (2022), who found that anthropomorphization of an AI-music algorithm led to higher acceptance of the algorithm as a true "musician," which in turn led to increased aesthetic appreciation. Collectively, these studies suggest that there may be an implicit role of effortfulness or embodiment behind people's evaluations of art.…”

Section: Studysupporting

confidence: 76%

Humans vs. AI: Whether and why we prefer human-created compared to AI-created artwork

Bellaiche

Shahi²,

Turpin³

et al. 2023

Preprint

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With the recent proliferation of advanced artificial intelligence (AI) models capable of mimicking human artworks, AI creations might soon replace products of human creativity, although skeptics argue that this outcome is unlikely. One possible reason this may be unlikely is that, independent of the physical properties of art, we place great value on the imbuement of the human experience in art. An interesting question, then, is whether and why people might prefer human- compared to AI-created artworks. To explore these questions, we manipulated the purported creator of pieces of art by randomly assigning a “Human-created” or “AI-created” label to paintings actually created by AI, and then assessed participants’ judgements of the artworks across four rating criteria (Liking, Beauty, Profundity, and Worth). Study 1 found increased positive judgements for human- compared to AI-labeled art across all criteria. Study 2 aimed to replicate and extend Study 1 with additional ratings (Emotion, Story, Meaningful, Effort, and Time to create) intended to elucidate why people more-positively appraise human-labeled artworks. The main findings from Study 1 were replicated, with narrativity (Story) and perceived effort behind artworks (Effort) moderating the label effects (“Human-created” vs. “AI-created”), but only for the sensory-level judgements (Liking, Beauty). Positive personal attitudes towards AI moderated label effects for more-abstract judgements (Profundity, Worth). These studies demonstrate that people tend to be negatively biased against AI-created artworks, which suggests that human engagement in the artistic process contributes positively to our appraisals of art and that, on this basis, AI may not replace human creativity.

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“…The generation of the confrontation network is the process of the game through G and D neural networks, which finally makes the two networks reach the optimal state. It generates a fake music generator and a high-level music discriminator [ 20 – 22 ]. The input music may be fake music or real music, which is identified by the discriminator.…”

Section: Methodsmentioning

confidence: 99%

The Psychological Education Strategy of Music Generation and Creation by Generative Confrontation Network under Deep Learning

Ge²,

Wang

2022

Computational Intelligence and Neuroscience

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In order to study the role of generative adversarial network (GAN) in music generation, this article creates a convolutional GAN-based Midinet as a baseline model through the music generation process and creative psychological education and GAN principle. Additionally, it proposes a music generation model based on music theory rules and a chord-constrained GAN dual-track music generation model. Based on this model, a deep chord gated recurrent neural generative adversarial network (DCG_GAN) is proposed. The generated melodies are evaluated in both subjective and objective directions. The results show that the three evaluation indicators of DCG_GAN have the highest scores in the subjective evaluation. The average score given by ordinary listeners reaches 3.76 points, and the professional score reaches 3.58 points, which are 0.69 and 1.31 points higher than the baseline model, respectively. In the objective evaluation, DCG_GAN is improved by 8.075% in empty bars rate (EBR). The UPC (num_chroma_used) evaluation index value of the DCG_GAN model is improved by 0.52 compared with the baseline model. The qualified note ratio (QNR) evaluation index value is improved by up to 4.46% among the five audio tracks. The proposed overall style-based music generation model has superior performance in music generation. Both subjective and objective evaluations show that the generated music is more favored by the audience, indicating that the combination of deep learning and GAN has a great effect on music generation.

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Human, I wrote a song for you: An experiment testing the influence of machines’ attributes on the AI-composed music evaluation

Cited by 29 publications

References 33 publications

Humans versus AI: whether and why we prefer human-created compared to AI-created artwork

Humans versus AI: whether and why we prefer human-created compared to AI-created artwork

Humans vs. AI: Whether and why we prefer human-created compared to AI-created artwork

The Psychological Education Strategy of Music Generation and Creation by Generative Confrontation Network under Deep Learning

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