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Noise estimation based on time-frequency correlation for speech enhancement

机译：基于时频相关的语音增强语音估计

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摘要

As a fundamental part of speech enhancement, noise estimation is particularly challenging in highly non-stationary noise environments. In this work, we propose an effective algorithm on the basis of the "Improved Minima Controlled Recursive Averaging (IMCRA)" with the objective to improve the performance of noise estimation. The main contributions of this work are: (i) in the algorithm, a rough decision about speech presence is proposed by calculating the autocorrelation and cross-channel correlation of the T-F (Time-Frequency) units; (ii) with this decision, we refine the smoothing parameters for the smoothing of noisy power spectrum and the recursive averaging in noise spectrum estimation as well as the weighting factor for the a priori SNR (Signal to Noise Ratio) estimation in the IMCRA; (iii) we improve the search of local minima during spectral bursts by adding a minimum search with a shorter window. Extensive experiments are carried out to evaluate the performance of our proposed algorithm. The experimental results illustrate that, compared with the IMCRA, the proposed approach significantly improves the accuracy of noise spectrum estimation and the quality of enhanced speech in the typical noise situations.
机译：作为语音增强的基本部分，在高度不稳定的噪声环境中，噪声估计尤其具有挑战性。在这项工作中，我们基于“改进的最小控制递归平均（IMCRA）”提出了一种有效的算法，目的是提高噪声估计的性能。这项工作的主要贡献是：（i）在算法中，通过计算T-F（时间-频率）单元的自相关和跨通道相关性，提出了有关语音存在的粗略决策； （ii）通过此决定，我们改进了用于噪声功率谱的平滑和噪声谱估计中的递归平均的平滑参数，以及IMCRA中先验SNR（信噪比）估计的加权因子； （iii）通过添加具有较短窗口的最小搜索来改善频谱突发期间的局部最小值搜索。进行了广泛的实验，以评估我们提出的算法的性能。实验结果表明，与IMCRA相比，该方法在典型的噪声情况下显着提高了噪声频谱估计的准确性和增强语音的质量。

著录项

  • 来源
    《Applied Acoustics》 |2013年第5期|770-781|共12页
  • 作者

    Wenhao Yuan; Jiajun Lin; Wei An; Yu Wang; Ning Chen;

  • 作者单位

    School of Information Science and Engineering, East China University of Science and Technology, Shanghai 200237, China;

    School of Information Science and Engineering, East China University of Science and Technology, Shanghai 200237, China;

    School of Information Science and Engineering, East China University of Science and Technology, Shanghai 200237, China;

    School of Information Science and Engineering, East China University of Science and Technology, Shanghai 200237, China;

    School of Information Science and Engineering, East China University of Science and Technology, Shanghai 200237, China;

  • 收录信息 美国《科学引文索引》(SCI);美国《工程索引》(EI);
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    noise estimation; speech enhancement; minimum search; improved minima controlled recursive; averaging;

    机译：噪声估计;语音增强;最少搜寻;改进的最小控制递归;平均;

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