Unbiased Noise Estimator for Q-Spectral Subtraction based Speech Enhancement

Rico Dahlan, Dikdik Krisnandi, A. Ramdan, H. Pardede
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引用次数: 2

Abstract

We present a technique for denoising speech using a modification of spectral subtraction method based on Tsallis statistics, q-SS. This technique works by assuming that noise power spectra are unknown but could be estimate. Since the target and interfering signal are not available, power spectra of both signal must be esimate from the mixed noisy signal. In this paper, we estimate the noise power spectra under MMSE criteria. Implementation of soft decision speech presence probability with fixed prior ensure that the estimator to be unbiased. Our experiment showed that the unbiased estimator signicantly improve q-SS filter performance.
基于q -谱减法的语音增强无偏噪声估计
我们提出了一种基于Tsallis统计量的谱减法的语音降噪技术,即q-SS。该技术的工作原理是假设噪声功率谱是未知的,但可以估计。由于目标信号和干扰信号不可用,因此必须从混合噪声信号中估计出两种信号的功率谱。本文在MMSE准则下估计了噪声功率谱。实现了具有固定先验的软决策语音存在概率,保证了估计量的无偏性。实验表明,无偏估计量显著提高了q-SS滤波器的性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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