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arXiv Machine Learning By Nizam Mohammed, Abu B. S. Rahman, Dimuthu D. K. Arachchige

Project Qualia: Recovering Experiential Music Structure from Session Co-occurrence Data

Read the original on arXiv Machine Learning →

Project Qualia investigates whether experiential similarity between songs can be extracted from listening behavior. Using 1.29 billion scrobbles from 9,396 users, the authors trained a Word2Vec model (Song2Vec) on session data, then applied an artist‑residual procedure to isolate artist‑independent signals. The residual embeddings still contained strong cross‑artist similarity, forming coherent genre and era clusters, demonstrating that experiential structure exists beyond artist identity.

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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