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A speech enhancement method based on sparse reconstruction on log-spectra

机译：基于稀疏重建对逻辑谱的语音增强方法

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摘要

A new speech enhancement method using sparse reconstruction of the log-spectra is presented. Similar to the traditional sparse coding methods, the proposed algorithm makes use of the least angle regression (LARS) with a coherence criterion (LARC) algorithm to reconstruct the log power spectrum of clean speech. However, a new stopping criterion is introduced to allow the LARC algorithm to adapt to various background noise environments. In addition, a modified two-step noise reduction with a iog-MMSE filter is applied which solves the bias of estimated a-priori signal-to-noise ratio (SNR). A notable improvement in the proposed algorithm over traditional speech enhancement methods is its adaptability to the changes in the SNR of noisy speech. The performance of the proposed algorithm is evaluated using standard measures based on a large set of speech and noise signals. The results show that a significant improvement is achieved compared to traditional approaches, especially in non-stationary noise environments where most traditional algorithms fail to perform.
机译：提出了一种新的语音增强方法，使用LOG-Spectra的稀疏重建。类似于传统的稀疏编码方法，所提出的算法利用具有相干标准（LARC）算法的最小角度回归（LAR）来重建清洁语音的日志功率谱。然而，引入了新的停止标准以允许LARC算法适应各种背景噪声环境。另外，应用具有IOG-MMSE滤波器的修改的两步降噪，其解决了估计的a-priori信噪比（SNR）的偏差。在传统语音增强方法上提出算法的显着改善是它对嘈杂语音的SNR变化的适应性。使用基于大量语音和噪声信号的标准度量来评估所提出的算法的性能。结果表明，与传统方法相比，尤其是在大多数传统算法未能执行的非平稳噪声环境中实现了显着的改进。

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