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
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.
By Elena Badillo-Goicoechea, Fengfeng He
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...
By Joanne Affolter, Benjamin Martin, Elena V. Epure, Gabriel Meseguer-Brocal, Fr\'ed\'eric Kaplan
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
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
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:2609.39552v1 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
arXiv:2607. 05469v1 Announce Type: cross Abstract: Unsupervised graph clustering is a fundamental technique for uncovering underlying semantic patterns in large-scale networks.
By Jingyun Zhang, Hao Peng, Jianxin Li, Angsheng Li, Philip S. Yu
arXiv:2506. 14293v4 Announce Type: replace-cross Abstract: We present Sleeping-DISCO 9M, a large-scale pre-training dataset for music and song.
By Tawsif Ahmed, Andrej Radonjic, Gollam Rabby
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.
By Spyridon Kantarelis, Ioannis Liolitsas, Konstantinos Thomas, Vassilis Lyberatos, Edmund Dervakos, Giorgos Stamou
arXiv:2608.23484v1 Announce Type: new
Abstract: We present Team Semiintelligencn's solution for the ACM RecSys 2026 TalkPlayData Challenge, addressing conversational music recommendation through a mu...
By Naman Garg, Sarika Jain, George Fazekas
The study investigates cultural biases in large language models (LLMs) by testing their ability to perform author profiling—inferring singers’ gender and ethnicity—from song lyrics in a zero‑shot setting. Evaluating over 10,000 lyrics across several open‑source models, the authors find that most LLMs default toward North American ethnicity, while DeepSeek‑1.5B leans toward Asian ethnicity, and that Ministral‑8B exhibits the strongest ethnicity bias whereas Gemma‑12B is the most balanced. The paper introduces two fairness metrics, Modality Accuracy Divergence (MAD) and Recall Divergence (RD), to quantify these disparities and provides code and results publicly on GitHub and HuggingFace.
By Valentin Lafargue, Ariel Guerra-Adames, Emmanuelle Claeys, Elouan Vuichard, Jean-Michel Loubes