Cortès Sebastià, GuillemMiron, MariusMolina, EmilioCiurana, AlexSerra, Xavier2025-10-152025-10-152025Cortès-Sebastià G, Miron M, Molina E, Ciurana A, Serra X. Enhanced television broadcast monitoring with source separation-assisted audio fingerprinting: a case study. Multimed Tools Appl. 2025 Oct 13. DOI: 10.1007/s11042-025-21080-x1380-7501http://hdl.handle.net/10230/71510Data de publicació electrònica 13-10-2025Music identification is crucial for distributing royalties in the music industry. This problem is solved using Audio fingerprinting (AFP) algorithms. However, these methods often struggle in real-world scenarios such as TV broadcasting, when music is in the background, masked by other sounds such as speech. While prior research has focused on improving AFP robustness to pitch and tempo variations, less attention has been given to enhancing robustness for background music identification. In this work, we assess whether source separation systems improve background music identification by recovering the music signal in these recordings. We present the first extensive study comprising 13 source separation algorithms and five AFP models. We evaluate them on a public dataset of TV recordings, assessing both music identification performance and computational cost. Our results show that source separation substantially improves peakbased AFP identifications, particularly when music is in the background. Additionally, this finding extends to foreground music, making the approach versatile for various music identification tasks, such as query-by-example. Deep learning-based model NeuralFP* (tailored for background music identification) shows no substantial benefit from adding a separation model as preprocessing. This reproducible study provides a comprehensive evaluation framework, offering valuable insights into using source separation methods to improve music identification in real-world contexts.application/pdfengThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.info:eu-repo/semantics/articlehttp://dx.doi.org/10.1007/s11042-025-21080-xAudio fingerprintingMonitoringBackground musicMusic loudnessSource separationinfo:eu-repo/semantics/openAccess