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Acoustic Event Detection (AED) is an important task of machine listening which, in recent years, has been addressed using common machine learning methods like Non-negative Matrix Factorization (NMF) or deep learning. However, most of these approaches do not take into consideration the way that human auditory system detects salient sounds. In this work, we propose a method for AED using weakly labeled data that combines a Non-negative Matrix Factorization model with a salience model based ondoi:10.1109/icassp.2019.8683586 dblp:conf/icassp/PodwinskaSFDP19 fatcat:fn6daaud3ja2lmhkyg46kbj5uy