Araz, Recep OguzCortès Sebastià, GuillemMolina, EmilioSerra, JoanSerra, XavierMitsufuji, YuhkiBogdanov, Dmitry2025-09-052025-09-052025Oguz Araz R, Cortès-Sebastià G, Molina E, Serra J, Serra X, Mitsufuji Y, Bogdanov D. Enhancing neural audio fingerprint robustness to audio degradation for music identification. Paper presented at: 26th International Society for Music Information Retrieval Conference (ISMIR 2025); 2025 Sep 21-25; Daejeon, Korea. 8p.http://hdl.handle.net/10230/71121Comunicació presentada al 26th International Society for Music Information Retrieval Conference (ISMIR 2025), celebrada a Daejeon (Korea) del 21 al 25 de setembre del 2025Audio fingerprinting (AFP) allows the identification of unknown audio content by extracting compact representations, termed audio fingerprints, that are designed to remain robust against common audio degradations. Neural AFP methods often employ metric learning, where representation quality is influenced by the nature of the supervision and the utilized loss function. However, recent work unrealistically simulates real-life audio degradation during training, resulting in sub-optimal supervision. Additionally, although several modern metric learning approaches have been proposed, current neural AFP methods continue to rely on the NT‑Xent loss without exploring the recent advances or classical alternatives. In this work, we propose a series of best practices to enhance the self-supervision by leveraging musical signal properties and realistic room acoustics. We then present the first systematic evaluation of various metric learning approaches in the context of AFP, demonstrating that a self‑supervised adaptation of the triplet loss yields superior performance. Our results also reveal that training with multiple positive samples per anchor has critically different effects across loss functions. Our approach is built upon these insights and achieves state-of-the-art performance on both a large, synthetically degraded dataset and a real-world dataset recorded using microphones in diverse music venues.application/pdfengLicensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0). Attribution: R. O. Araz, G. Cortès-Sebastià, E. Molina, J. Serrà, X. Serra, Y. Mitsufuji, and D. Bogdanov, “Enhancing Neural Audio Fingerprint Robustness to Audio Degradation for Music Identification”, in Proc. of the 26th Int. Society for Music Information Retrieval Conf., Daejeon, South Korea, 2025.Enhancing neural audio fingerprint robustness to audio degradation for music identificationinfo:eu-repo/semantics/conferenceObjectNeural audio fingerprintMusic identificationAudio robustnessAudio degradationinfo:eu-repo/semantics/openAccess