Perceptual Properties Based Signal Subspace Microphone Array Speech Enhancement Algorithm
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摘要: 针对麦克风阵列信号子空间语音增强算法的不足, 结合人耳的听觉掩蔽效应, 提出了改进的信号子空间算法. 提出了通过置信度判断来确定噪声子空间维度的方法, 在噪声子空间上, 通过条件概率的方法估计出噪声功率谱. 在此基础上, 结合人耳的听觉掩蔽效应给出了线性滤波器的一种合理估计. 实验结果表明所提的方法相对于传统算法, 更有效地抑制了噪声, 在多项语音质量评价指标上都有明显的改进.Abstract: To aim at the drawbacks of the conventional subspace microphone array speech enhancement method, some improvements are proposed by using masking properties of human ears. Confidence function is used to determine the noise subspace dimension. In noise subspace, the noise power spectrum is estimated by the conditional likelihood. Then, the masking properties are incorporated into the subspace method to estimate the linear filter. Experiments show that compared with conventional algorithms, the proposed approach suppresses noise more effectively and obtains a significant improvement on objective speech quality measures.
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Key words:
- Speech enhancement /
- signal subspace /
- microphone array /
- masking properties /
- eigen-decomposition
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