arXiv:2506. 14293v4 Announce Type: replace-cross Abstract: We present Sleeping-DISCO 9M, a large-scale pre-training dataset for music and song.
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.
arXiv:2608. 06165v1 Announce Type: cross Abstract: Existing audio-to-score (A2S) systems primarily focus on classical music, and the application to popular music remains underexplored.
arXiv:2609.39552v1 Announce Type: cross Abstract: Text-to-song generation models can be prompted to imitate specific artists or regurgitate entire songs from their training data. Although these pheno...
arXiv:2601.11262v2 Announce Type: replace-cross Abstract: Music Cover Retrieval, also known as Version Identification, aims to recognize distinct renditions of the same underlying musical work, a tas...
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.
Chordonomicon is a new dataset of over 666,000 song-level symbolic chord progressions, each annotated with structural parts such as verse, chorus, and bridge, as well as genre and release date. The dataset was compiled by scraping user-generated progressions from multiple sources and shows strong similarity to established prior datasets. The authors also provide a reproducible benchmark suite for next chord prediction, evaluating RNN, GRU, and LSTM models across various context windows and data scales, and find that structural part annotations consistently improve prediction performance.
arXiv:2605. 03395v2 Announce Type: replace-cross Abstract: Music popularity prediction has attracted growing research interest, with relevance to artists, platforms, and recommendation systems.