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Please use this identifier to cite or link to this item: http://hdl.handle.net/UCSP/15878
Title: NMF-based temporal feature integration for acoustic event classification
Authors: Ludeña Choez, Jimmy Diestin
Gallardo Antolín, Ascensión
Keywords: Factorization;Frequency bands;Integration;Support vector machines;Acoustic event classification;Classification performance;Filter-bank coefficients;Music genre classification;Nonnegative matrix factorization;Temporal feature integrations;Temporal information;Unsupervised method;Filter banks
Issue Date: 2013
Publisher: International Speech and Communication Association
metadata.dc.relation.uri: https://www.scopus.com/inward/record.uri?eid=2-s2.0-84906264286&partnerID=40&md5=d8c5924415a4fb87e1b53f9ae9b60552
Abstract: In this paper, we propose a new front-end for Acoustic Event Classification tasks (AEC) based on the combination of the temporal feature integration technique called Filter Bank Coefficients (FC) and Non-Negative Matrix Factorization (NMF). FC aims to capture the dynamic structure in the short-term features by means of the summarization of the periodogram of each short-term feature dimension in several frequency bands using a predefined filter bank. As the commonly used filter bank has been devised for other tasks (such as music genre classification), it can be suboptimal for AEC. In order to overcome this drawback, we propose an unsupervised method based on NMF for learning the filters which collect the most relevant temporal information in the short-time features for AEC. The experiments show that the features obtained with this method achieve significant improvements in the classification performance of a Support Vector Machine (SVM) based AEC system in comparison with the baseline FC features. Copyright © 2013 ISCA.
URI: http://repositorio.ucsp.edu.pe/handle/UCSP/15878
ISSN: 2308457X
Appears in Collections:Artículos de investigación

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