ArticleFrontiers in artificial intelligence2023
Accurately predicting hit songs using neurophysiology and machine learning.
Article in Frontiers in artificial intelligence, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Explicit encoding of temporal dynamics for interpretable melody similarity modeling: insight into machine learning models.Frontiers in computational neuroscience · 2026Article
- A video intervention reduces racial bias in a representative sample of US adults: A brain as predictor study.PloS one · 2026Article
- Virtual Reality Education Increases Neurologic Immersion and Empathy in Nursing Students.Nursing reports (Pavia, Italy) · 2025Article
- The contagion of neurologic Immersion predicts retail purchases.Frontiers in neuroscience · 2025Article
- Continuous remote monitoring of neurophysiologic Immersion accurately predicts mood.Frontiers in digital health · 2024Article
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Identifying hit songs is notoriously difficult. Traditionally, song elements have been measured from large databases to identify the lyrical aspects of hits. We took a different methodological approach, measuring neurophysiologic responses to a set of songs provided by a streaming music service that identified hits and flops. We compared several statistical approaches to examine the predictive accuracy of each technique. A linear statistical model using two neural measures identified hits with 69% accuracy. Then, we created a synthetic set data and applied ensemble machine learning to capture inherent non-linearities in neural data. This model classified hit songs with 97% accuracy. Applying machine learning to the neural response to 1st min of songs accurately classified hits 82% of the time showing that the brain rapidly identifies hit music. Our results demonstrate that applying machine learning to neural data can substantially increase classification accuracy for difficult to predict market outcomes.
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