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arXiv Machine Learning By Elena Badillo-Goicoechea, Fengfeng He

Recovering Expert Critic-Sourced Network Adjacency between Musical Artists from Acoustic Distributions: A Construct-Validity Approach

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The paper investigates whether expert critic‑sourced relationships between musical artists—called critical adjacency—can be recovered from the artists’ acoustic content. By modeling artists as distributions over 80 low‑level acoustic descriptors and measuring pairwise proximity with Wasserstein distances, the authors achieve an out‑of‑sample AUC of 0.767 for predicting critic‑linked edges, with higher recoverability for edges supported by multiple critics. The results suggest that critical discourse contains a reproducible sonic core, while sociological factors also influence the perceived adjacency between artists.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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