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.
By Nizam Mohammed, Abu B. S. Rahman, Dimuthu D. K. Arachchige
arXiv:2602.12881v2 Announce Type: replace-cross
Abstract: We examine how lyric representation shapes computational similarity using four 384-dimensional multilingual MiniLM representations of 7,891 K...
By Oktay Karaku\c{s}, Can Ey\"upo\u{g}lu
arXiv:2607. 03296v1 Announce Type: cross Abstract: Crossmodal correspondences between sound and taste are well established in psychology and neuroscience, but largely absent from content-based multimedia retrieval.
By Matteo Spanio, Antonio Rod\`a
The paper introduces the MATCHA dataset, comprising 1,105 perceptual assessments from 83 experts on attribute-based music matches across five musical attributes—melody, harmony, rhythm, voice, and timbre. A triplet-based forced-choice experiment with 300 cases, including plagiarism, cover songs, and AI-generated music, was used to gather these judgments. Results show measurable agreement among participants and partial alignment with computational similarity measures, highlighting the need for perceptually grounded evaluation in generative AI for music.
By Roser Batlle-Roca, Woosung Choi, Joan Serr\`a, Fabio Morreale, Wei-Hsiang Liao, Xavier Serra, Emilia G\'omez, Yuki Mitsufuji
MUUNRiver-Bench is a diagnostic benchmark for music retrieval that uses natural‑language instructions to define relevance for reference‑audio queries. It contains 3,440 tracks across 13 genres and 116 sub‑genres and covers seven tasks such as similar‑music, style‑preserving lyric‑rewriting, cover, and segment retrieval. Experiments with six models in eight configurations show that acoustic encoders favor local identity while text‑aligned encoders favor semantic relations, and that instruction‑aware and audio‑text fusion systems do not consistently outperform their backbones.
By Zhancheng Guo, Congren Dai, Shangda Wu, Jianhuai Hu, Danni Zhao, Xiaobing Li, Maosong Sun
arXiv:2609.23877v1 Announce Type: new
Abstract: Modern music streaming platforms face a persistent tradeoff: exploiting familiar content versus driving the exploration of novel items. While users fre...
By Xiao Liu, Yanwei Song, Srivaths Ranganathan, Yuan Chen, Zheyun Feng, Parker Steenburgh, Jochen Klingenhoefer, Nathan Lasche, Gergo Varady, Tim Steele
The paper evaluates Graph Neural Networks (GNNs) for predicting artist success within collaboration networks, extending prior work on Italian and Danish music scenes by adding a Polish dataset and merging the three into a tri‑national network. Statistical analysis shows the Polish and combined networks share similar clustering properties, while predictive experiments reveal that GNNs match or slightly outperform a Multilayer Perceptron (MLP) in some cases but the MLP generally yields higher success metrics. The findings suggest that internal node attributes such as genre and label affiliation may be more predictive than network topology, and that GNNs may better capture cross‑border relational structures in the merged network.
By Wiktor Dowgia{\l}{\l}o
arXiv:2608. 05153v1 Announce Type: cross Abstract: GraphRAG underperforms vector RAG on citation precision in many reports, but where and why have remained corpus-bound.
By Meftun Akarsu, Burak Ozdemir
The paper introduces CreativePreferences, a large dataset of 2.8 million texts labeled by 317 million human preference judgments across seven creative domains, and explores how preference components that are tacit, articulable, and verifiable differ. By modeling these labels with executable programs, rubric banks, and densely trained models, the authors identify robust articulability and verifiability gaps—especially in domains like peer review and creative writing—and propose a novel measurement approach to estimate these gaps. The study demonstrates that fully articulated models can diverge from human preferences and that articulating preferences may shift them away from tacit dimensions, offering recommendations on task prompting and learning strategies.
"whyItMatters":"The findings highlight that current AI preference modeling may miss crucial tacit aspects, potentially leading to misaligned or suboptimal outcomes in creative and evaluative tasks."
By Alexander Spangher, Sheldon Huang, Andreas Haupt, Noah D. Goodman, Diyi Yang, Daniel E. Ho, Sanmi Koyejo
arXiv:2605. 03395v2 Announce Type: replace-cross Abstract: Music popularity prediction has attracted growing research interest, with relevance to artists, platforms, and recommendation systems.
By Jaavid Aktar Husain, Dorien Herremans
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...
By Arhan Vohra, Choenden Kyirong, Laura Ib\'a\~nez-Mart\'inez, Mart\'in Rocamora
TrueMuse is a new benchmark designed to evaluate data attribution in text-to-music models. It consists of a controlled dataset created by fine‑tuning three diffusion‑based models on curated attribution samples, providing known attribution targets. The benchmark covers four settings—melodic structure, timbral characteristics, artist‑level style, and genre‑level patterns—across 133 attributes, 648 models, and 95,456 generated samples, and is used to assess existing black‑box attribution methods along several dimensions.
By Jiawei Yu, Jian Liu