ASIA unversity:Item 310904400/102193
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    题名: Estimation of noise magnitude for speech denoising using minima-controlled-recursive-averaging algorithm adapted by harmonic properties
    作者: 陸清達;Lu, Ching-Ta;*;雷忠霖;Lei, Chung-Lin;沈俊宏;SHEN, JUN-HONG;王玲玲;Wang, Ling-Ling;曾崑福;Tseng, Kun-Fu
    贡献者: 資訊傳播學系
    日期: 2017-01
    上传时间: 2017-03-01 14:48:58 (UTC+8)
    摘要: The accuracy of noise estimation is important for the performance of a speech denoising system. Most noise estimators suffer from either overestimation or underestimation on the noise level. An overestimate on noise magnitude will cause serious speech distortion for speech denoising. Conversely, a great quantity of residual noise will occur when the noise magnitude is underestimated. Accurately estimating noise magnitude is important for speech denoising. This study proposes employing variable segment length for noise tracking and variable thresholds for the determination of speech presence probability, resulting in the performance improvement for a minima-controlled-recursive-averaging (MCRA) algorithm in noise estimation. Initially, the fundamental frequency was estimated to determine whether a frame is a vowel. In the case of a vowel frame, the increment of segment lengths and the decrement of threshold for speech presence were performed which resulted in underestimating the level of noise magnitude. Accordingly, the speech distortion is reduced in denoised speech. On the contrary, the segment length decreases rapidly in noise-dominant regions. This enables the noise estimate to update quickly and the noise variation to track well, yielding interference noise being removed effectively through the process of speech denoising. Experimental results show that the proposed approach has been effective in improving the performance of the MCRA algorithm by preserving the weak vowels and consonants. The denoising performance is therefore improved. View Full-Text
    關聯: Applied Sciences-Basel
    显示于类别:[資訊傳播學系] 期刊論文

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