Acoustic Event Detection Method Using Semi-supervised Non-negative Matrix Factorizationwith a Mixture of Local Dictionaries

This paper proposes an acoustic event detection (AED) method using semi-supervised non-negative matrix factorization (NMF) with a mixture of local dictionaries (MLD). The proposed method based on semi-supervised NMF newly introduces a noise dictionary and a noise activation matrix both dedicated to unknown acoustic atoms which are not included in the MLD. Because unknown acoustic atoms are better modeled by the new noise dictionary learned upon classification and the new activation matrix, the proposed method provides a higher classification performance for event classes modeled by the MLD when a signal to be classified is contaminated by unknown acoustic atoms. Evaluation results using DCASE2016 task 2 Dataset show that F-measure by the proposed method with semi-supervised NMF is improved by as much as 11.1% compared to that by the conventional method with supervised NMF.