2020
DOI: 10.1007/s11042-020-08748-2
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Mixture linear prediction Gammatone Cepstral features for robust speaker verification under transmission channel noise

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Cited by 10 publications
(3 citation statements)
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“…The Gammatone frequency filter banks are indeed a group of cochlear modeling filter banks [39,40]. A Gammatone filter bank frequency selection is very close to the characteristics of an average human ear filter.…”
Section: Enhanced Gamma-tone Filter Bank Implementationmentioning
confidence: 96%
“…The Gammatone frequency filter banks are indeed a group of cochlear modeling filter banks [39,40]. A Gammatone filter bank frequency selection is very close to the characteristics of an average human ear filter.…”
Section: Enhanced Gamma-tone Filter Bank Implementationmentioning
confidence: 96%
“…Dance features can be divided into continuous speech features, sound quality features, spectralbased features and non-linear teager energy operator (TEO)based features. Common spectral features include Linear Prediction Cepstral Coefficient (LPCC) (Krobba et al, 2020), Mel Frequency Cepstral Coefficient (MFCC) (Albadr et al, 2021), Log Frequency Power Coefficient (LFPC) (Gao, 2022). LPCC linearly approximates speech at all frequencies, which is inconsistent with human auditory characteristics.…”
Section: Features Of Dance Emotionmentioning
confidence: 99%
“…Most feature extraction methods use the Karhunen-Loeve Transform (KLT) [9,10]. With outstanding results, these methods were applied to cases of text-independent speaker recognition [8].…”
Section: Introductionmentioning
confidence: 99%