Variable-Length Audio Fingerprinting (VLAFP) is a new deep learning model that converts audio into low-dimensional fingerprints while allowing variable-length input, unlike existing methods that require fixed-length segments. By supporting variable-length audio during both training and testing, VLAFP captures temporal dynamics that were previously neglected. Experiments demonstrate that VLAFP outperforms current state‑of‑the‑art systems in live audio identification and audio retrieval across three real‑world datasets.
By Hongjie Chen, Hanyu Meng, Huimin Zeng, Ryan A. Rossi, Lie Lu, Josh Kimball
arXiv:2608. 14916v1 Announce Type: cross Abstract: AI-generated music detectors are commonly evaluated against original songs, but real-world uploads are often remixed, re-encoded, pitch-shifted, or otherwise edited.
By Alexandru-Stefan Morosanu, Valerian Cecan, Stefan-Daniel Achirei, Laura Erhan
arXiv:2606. 17301v1 Announce Type: cross Abstract: Search, a foundational operation in computer science, maps a query to a matching item in a collection.
By Muhammad Taimoor Haseeb, Ahmad Hammoudeh, Gus Xia
arXiv:2603. 23667v2 Announce Type: replace-cross Abstract: We introduce Echoes, a new dataset for music deepfake detection designed for training and benchmarking detectors under realistic and provider-diverse conditions.
By Octavian Pascu, Dan Oneata, Horia Cucu, Nicolas M. Muller
arXiv:2512. 10120v2 Announce Type: replace-cross Abstract: General-purpose audio representations aim to map acoustically variable instances of the same event to nearby points, resolving content identity in a zero-shot setting.
By Maris Basha, Anja Zai, Sabine Stoll, Richard Hahnloser
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.
By Austin Rockman
TUTTI is a new pre‑training framework for audio‑to‑score transcription that uses a large, fully synthetic multi‑instrument dataset generated by a symbolic music model. The approach trains a standard Transformer encoder‑decoder on these synthetic audio‑score pairs, producing a stronger foundational representation than single‑instrument training. When fine‑tuned on real datasets, TUTTI surpasses prior methods, achieving state‑of‑the‑art results and demonstrating strong cross‑instrument transferability.
By Jianhuai Hu, Yashan Wang, Shangda Wu, Zhancheng Guo, Shijie Liang, Wuna Meng, Chuanqi Yang, Xiaobing Li, Feng Yu, Maosong Sun
Generalizable Audio-to-Score (A2S) transcription is fundamentally constrained by the severe scarcity of high-quality, real-world paired data. Relying solely on existing human-annotated datasets often...
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.
By \c{C}a\u{g}r{\i} Eser
arXiv:2606. 16612v1 Announce Type: cross Abstract: The rapid advancement of AI music generators highlights the urgent need for reliable Synthetic Song Detection (SSD).
By Yan Han, Zhibin Wen, Yuan Wang, Shuangrun Shao, Xiaobing Li, Yang Xu, Wei Li
arXiv:2606. 30700v1 Announce Type: cross Abstract: Self-supervised learning enables audio representations that transfer across domains and tasks.
By Ludovic K. Tuncay (IRIT-SAMoVA), Etienne Labb\'e (IRIT-SAMoVA), Thomas Pellegrini (IRIT-SAMoVA)
arXiv:2510. 02916v2 Announce Type: replace-cross Abstract: We propose SALSA-V, a multimodal video-to-audio generation model capable of synthesizing highly synchronized, high-fidelity long-form audio from silent video content.
By Amir Dellali, Luca A. Lanzend\"orfer, Florian Gr\"otschla, Roger Wattenhofer