2026-04-14T03:50:21Zhttps://recercat.cat/oai/request

oai:recercat.cat:10230/715102025-10-18T20:14:53Zcom_2072_6col_2072_452952

00925njm 22002777a 4500 dc Miron, Marius author Cortès Sebastià, Guillem author Molina, Emilio author Ciurana, Alex author Serra, Xavier author 2025-10-15T12:26:17Z 2025-10-15T12:26:17Z 2025 Data de publicació electrònica 13-10-2025 Music 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. This research is part of NextCore – New generation of music monitoring technology (RTC2019-007248-7) funded by the Spanish Ministerio de Ciencia e Innovación and the Agencia Estatal de Investigación; and resCUE – Smart system for automatic usage reporting of musical works in audiovisual productions (SAV20221147) funded by CDTI and the European Union - Next Generation EU, and supported by the Spanish Ministerio de Ciencia, Innovación y Universidades and the Ministerio para la Transformación Digital y de la Función Pública. Audio fingerprinting Monitoring Background music Music loudness Source separation Enhanced television broadcast monitoring with source separation-assisted audio fingerprinting: a case study