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Blind Dereverberation Based on CMN and Spectral Subtraction byMulti-channel LMS Algorithm

机译：基于CMN的盲人DERERATERATION和频谱减法对MULTI通道LMS算法

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We proposed a blind dereverberation method based on spectral subtraction by Multi-Channel Least Mean Square (MCLMS) al-gorithm for distant-talking speech recognition in our previous study [1]. In this paper, we discuss the problems of the pro-posed method and present some solutions. In a distant-talking environment, the length of channel impulse response is longer than the short-term spectral analysis window. By treating the late reverberation as additive noise, a noise reduction technique based on spectral subtraction was proposed to estimate power spectrum of the clean speech using power spectra of the dis-torted speech and the unknown impulse responses. To estimate the power spectra of the impulse responses, a Variable Step-Size Unconstrained MCLMS (VSS-UMCLMS) algorithm for iden-tifying the impulse responses in a time domain was extended to a frequency domain. To reduce the effect of the estimation error of channel impulse response, we normalize the early re-verberation by CMN instead of the spectral subtraction used by the estimated impulse response in this paper. Furthermore, our proposed method is combined with a conventional delay-and-sum beamforming. We conducted the experiments on distorted speech signal simulated by convolving multi-channel impulse responses with clean speech. The modified proposed method achieved a relative error reduction rate of 22.7% from conven-tional CMN and 12.0% from the original proposed method, re-spectively. By combining the modified proposed method with the beamforming, a furthermore improvement (relative error re-duction rate of 23.3%) was achieved.
机译：我们提出了一种基于多声道最低均线（MCLMS）AL-Gorithm的横向减法的盲人DEREERATION方法，在我们之前的研究中进行了遥远的语音识别[1]。在本文中，我们讨论了Pro-adosed方法的问题并呈现了一些解决方案。在遥远的环境中，信道脉冲响应的长度长于短期谱分析窗口。通过将后期混响作为附加噪声处理，提出了一种基于光谱减法的降噪技术来使用DIS - 倾倒语音的功率谱和未知的脉冲响应来估计清洁语音的功率谱。为了估计脉冲响应的功率谱，用于识别时域中的脉冲响应的可变阶梯大小的未受控MCLM（VSS-UMCLMS）算法被扩展到频域。为了减少信道脉冲响应的估计误差的效果，我们通过本文的估计脉冲响应的频谱减法来标准化CMN的早期重搏。此外，我们所提出的方法与传统的延迟和总和波束形成相结合。我们通过用清洁语音卷积多通道脉冲响应进行模拟的扭曲语音信号的实验。改进的提出方法从最初的提出方法获得了相对误差降低22.7％和12.0％，重新定步。通过将改进的提出方法与波束形成相结合，实现了进一步的改进（相对误差再加率为23.3％）。

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