2020
DOI: 10.3390/app10207385
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A Preprocessing Strategy for Denoising of Speech Data Based on Speech Segment Detection

Abstract: In this paper, we propose a preprocessing strategy for denoising of speech data based on speech segment detection. A design of computationally efficient speech denoising is necessary to develop a scalable method for large-scale data sets. Furthermore, it becomes more important as the deep learning-based methods have been developed because they require significant costs while showing high performance in general. The basic idea of the proposed method is using the speech segment detection so as to exclude non-spe… Show more

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Cited by 7 publications
(2 citation statements)
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“…Feature extraction is accomplished by changing the speech waveform to a form of parametric representation at a relatively lesser data rate for subsequent processing and analysis [ 11 , 12 , 13 , 14 ]. Feature extraction approaches usually yield a multidimensional feature vector for every speech signal.…”
Section: Related Workmentioning
confidence: 99%
“…Feature extraction is accomplished by changing the speech waveform to a form of parametric representation at a relatively lesser data rate for subsequent processing and analysis [ 11 , 12 , 13 , 14 ]. Feature extraction approaches usually yield a multidimensional feature vector for every speech signal.…”
Section: Related Workmentioning
confidence: 99%
“…It is based on principal component analysis (PCA) for speech denoising. [8] have introduced a preprocessing method for denoising speech. The procedure eliminates nonspeech segments with a slight cost using speech segment detection before starting the denoising process.…”
Section: Introductionmentioning
confidence: 99%