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On the Joint Use of NMF and Classification for Overlapping Acoustic Event Detection

1
School of ECE, National Technical University of Athens, 15773 Athens, Greece
2
Athena Research and Innovation Center, 15125 Maroussi, Greece
3
Department of ECE, University of Thessaly, 38221 Volos, Greece
*
Author to whom correspondence should be addressed.
Presented at the International Workshop on Computational Intelligence for Multimedia Understanding (IWCIM), Kos Island, Greece, 2 September 2017.
Proceedings 2018, 2(2), 90; https://doi.org/10.3390/proceedings2020090
Published: 9 January 2018
In this paper, we investigate the performance of classifier-based non-negative matrix factorization (NMF) methods for detecting overlapping acoustic events. We provide evidence that the performance of classifier-based NMF systems deteriorates significantly in overlapped scenarios in case mixed observations are unavailable during training. To this end, we propose a K-means based method for artificial generation of mixed data. The method of Mixture of Local Dictionaries (MLD) is employed for the building of the NMF dictionary using both the isolated and artificially mixed data. Finally an SVM classifier is trained for each of the isolated and mixed event classes, using the corresponding MLD-NMF activations from the training set. The proposed system, tested on two experiments with (a) synthetic and (b) real events, outperforms the state-of-the-art classifier-based NMF system in the overlapped scenarios.
Keywords: NMF; MLD; mixed data; overlapping acoustic event detection NMF; MLD; mixed data; overlapping acoustic event detection
MDPI and ACS Style

Giannoulis, P.; Potamianos, G.; Maragos, P. On the Joint Use of NMF and Classification for Overlapping Acoustic Event Detection. Proceedings 2018, 2, 90.

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