arXiv Machine Learning
Scalable Music Cover Retrieval Using Lyrics-Aligned Audio Embeddings
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arXiv AI
Sep 16
MUUNRiver-Bench: Diagnosing Relation-Dependent Music Retrieval with Multimodal Instructions
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.
arXiv Machine Learning
Oct 8
Towards AI-Generated Music Plagiarism Detection as a Version Identification Problem
The paper addresses the challenge of detecting plagiarism in music generated by AI, noting that traditional binary notions of plagiarism are insufficient due to nuanced musical elements. It evaluates whether existing music version identification models, originally designed for human covers, can transfer to human-to-AI plagiarism detection. The authors introduce COPYCAT, a large benchmark of 350,654 pairs derived from real plagiarism cases and augmented with generative re‑synthesis and DSP obfuscations, and demonstrate that a supervised embedding‑shift approach significantly improves detection performance over simple distance thresholding.
Hugging Face Trending Papers
Oct 6
Towards AI-Generated Music Plagiarism Detection as a Version Identification Problem
The rapid expansion of text-to-music generative models challenges traditional paradigms of music creation and intellectual property. Plagiarism in this context is rarely an absolute mathematical binar...
arXiv Machine Learning
Jun 24
SLEEPING-DISCO 9M: A large-scale pre-training dataset for generative music modeling
arXiv:2506. 14293v4 Announce Type: replace-cross Abstract: We present Sleeping-DISCO 9M, a large-scale pre-training dataset for music and song.
arXiv Machine Learning
Jul 2
Evaluating Pretrained Music Embeddings for Cross-Performance Jazz Standard Recognition
arXiv:2607. 00777v1 Announce Type: cross Abstract: Recognizing jazz standards from audio is a challenging form of tune-level music retrieval: different performances of the same standard may vary in tempo, key, arrangement, instrumentation, improvisational content, and even whether the head melody is present.
arXiv Machine Learning
Jul 27
Reflector: Arrangement-Aware Harmonic Retrieval for Sample-Based Composition
arXiv:2607. 22413v1 Announce Type: cross Abstract: Sample retrieval tools can help composers find harmonically compatible material, but querying from a fixed reference sample becomes less informative as arrangements evolve and the harmonic context shifts with each musical decision.