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- W2088489891 abstract "This paper presents a new robust feature extraction algorithm based on a modified approach to power bias subtraction combined with applying a threshold to the power spectral density. Power bias level is selected as a level above which the signal power distribution is sharpest. The sharpness is measured using the ratio of arithmetic mean to the geometric mean of medium-duration power. When subtracting this bias level, power flooring is applied to enhance robustness. These new ideas are employed to enhance our recently introduced feature extraction algorithm PNCC (Power Normalized Cepstral Coefficient). While simpler than our previous PNCC, experimental results show that this new PNCC is showing better performance than our previous implementation." @default.
- W2088489891 created "2016-06-24" @default.
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- W2088489891 date "2010-01-01" @default.
- W2088489891 modified "2023-10-10" @default.
- W2088489891 title "Feature extraction for robust speech recognition based on maximizing the sharpness of the power distribution and on power flooring" @default.
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- W2088489891 doi "https://doi.org/10.1109/icassp.2010.5495570" @default.
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