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- W2001108690 abstract "HighlightsWe propose a new front-end for Acoustic Event Classification tasks (AEC).The proposed front-end relies on the high pass filtering of acoustic event signals.Results show that it outperforms the baseline system in clean and noisy conditions. 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 100-275Hz in the feature extraction process in clean conditions and below 400-500Hz in noisy conditions, improves significantly the performance of the system with respect to the baseline." @default.
- W2001108690 created "2016-06-24" @default.
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- W2001108690 date "2015-03-01" @default.
- W2001108690 modified "2023-10-16" @default.
- W2001108690 title "Feature extraction based on the high-pass filtering of audio signals for Acoustic Event Classification" @default.
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- W2001108690 doi "https://doi.org/10.1016/j.csl.2014.04.001" @default.
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