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• W2072941421 abstract "Conventional single-channel noise reduction algorithms typically have problems with non-stationary noise. Popular algorithms such as minimum statistics or voice-activity-detector-based methods rely on the assumption that the noise spectral characteristics change very slowly over time. Codebook-based approaches try to overcome this problem by incorporating a priori knowledge about speech and different noise types. These approaches perform a joint estimation of the speech and noise spectra on a frame-by-frame basis. The frames are typically 20–40 ms long so that fast fluctuations of the signal characteristics can be tracked instantaneously. However, these methods require a pitch estimator to prevent speech distortion as well as residual noise in voiced speech frames. In addition, they are not very robust against model mismatch. In this paper, we propose an integrated noise estimation algorithm that combines the ability of codebook-based algorithms to track non-stationary noise with the robustness of a recursive minimum-tracking-based noise estimation algorithm. An objective and subjective evaluation is provided. Results confirm the superiority of the proposed algorithm in non-stationary noise scenarios compared to state-of-the-art algorithms." @default.
• W2072941421 created "2016-06-24" @default.
• W2072941421 creator A5071361210 @default.
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• W2072941421 date "2012-03-01" @default.
• W2072941421 modified "2023-09-27" @default.
• W2072941421 title "Integrating recursive minimum tracking and codebook-based noise estimation for improved reduction of non-stationary noise" @default.
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• W2072941421 doi "https://doi.org/10.1016/j.sigpro.2011.09.021" @default.
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