RT Journal Article
T1 Feature extraction based on the high-pass filtering of audio signals for Acoustic Event Classification
A1 Ludeña Choez, Jimmy D.
A1 Gallardo Antolín, Ascensión
AB 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.
PB Elsevier
SN 0885-2308
YR 2015
FD 2015-03
LK https://hdl.handle.net/10016/21548
UL https://hdl.handle.net/10016/21548
LA eng
NO This work has been partially supported by the Spanish Government grants IPT-120000-2010-24 and TEC2011-26807. Financial support from the Fundación Carolina and Universidad Católica San Pablo, Arequipa.
DS e-Archivo
RD 20 may. 2024