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Please use this identifier to cite or link to this item: http://hdl.handle.net/UCSP/15857
Title: Feature extraction based on the high-pass filtering of audio signals for Acoustic Event Classification
Authors: Ludeña Choez, Jimmy
Gallardo Antolín, Ascensión
Keywords: Audio acoustics;Extraction;Feature extraction;Speech recognition;Acoustic event classification;Acoustic events;Auditory filterbank;High-pass filtering;Mel-frequency cepstral coefficients;Noisy conditions;Spectral characteristics;Speech spectra;Signal processing
Issue Date: 2015
Publisher: Academic Press
metadata.dc.relation.uri: https://www.scopus.com/inward/record.uri?eid=2-s2.0-84913604166&doi=10.1016%2fj.csl.2014.04.001&partnerID=40&md5=c7d94b25cdef4e49d30c8610a19ed580
Abstract: In this paper, we propose a new front-end for Acoustic Event Classification tasks (AEC). First, we study the spectral characteristics of different acoustic events in comparison with the structure of speech spectra. Second, from the findings of this study, we propose a new parameterization for AEC, which is an extension of the conventional Mel-Frequency Cepstral Coefficients (MFCC) and is based on the high pass filtering of the acoustic event signal. The proposed front-end have been tested in clean and noisy conditions and compared to the conventional MFCC in an AEC task. Results support the fact that the high pass filtering of the audio signal is, in general terms, beneficial for the system, showing that the removal of frequencies below 100-275 Hz in the feature extraction process in clean conditions and below 400-500 Hz in noisy conditions, improves significantly the performance of the system with respect to the baseline. © 2014 Elsevier Ltd. All rights reserved.
URI: http://repositorio.ucsp.edu.pe/handle/UCSP/15857
ISSN: 8852308
Appears in Collections:Artículos - Ingeniería Electrónica y de Telecomunicaciones

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