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首页> 外文会议>2014 International Conference on Electrical Sciences and Technologies in Maghreb >LPCs enhancement in iterative Kalman filtering for speech enhancement using overlapped frames
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LPCs enhancement in iterative Kalman filtering for speech enhancement using overlapped frames

机译：使用重叠帧进行语音增强的迭代卡尔曼滤波中的LPC增强

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摘要

In this work, we are concerned by a new iterative Kalman filtering scheme where a linear predictor model parameters are estimated from noisy speech. However, when only noise-corrupted speech is available, the enhancement performance of the Kalman filter is somewhat dependent on the accuracy of the LPC and excitation variance estimates. Nevertheless, linear prediction based speech (LPC) analysis is known to be sensitive to the presence of additive noise. To overcome this problem we present in this paper an analysis and application of the iterative Kalman filtering with overlapped frames. Our enhancement experiments use a NOIZEUS corpus where the proposed method achieves higher Perceptual Evaluation of Speech Quality (PESQ) score and better subjective tests than the iterative scheme of Gibson as well as other enhancement methods.
机译：在这项工作中，我们关注一种新的迭代卡尔曼滤波方案，该方案从噪声语音中估计出线性预测模型参数。但是，当只有噪声损坏的语音可用时，卡尔曼滤波器的增强性能在某种程度上取决于LPC的准确性和激励方差估计。尽管如此，已知基于线性预测的语音（LPC）分析对加性噪声的存在很敏感。为了克服这个问题，我们在本文中对具有重叠帧的迭代卡尔曼滤波进行了分析和应用。我们的增强实验使用NOIZEUS语料库，其中所提出的方法比Gibson的迭代方案以及其他增强方法获得更高的语音质量感知评估（PESQ）分数和更好的主观测试。

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