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A microphone array system integrating beamforming, feature enhancement, and spectral mask-based noise estimation

机译:集成了波束成形,功能增强和基于频谱模板的噪声估计的麦克风阵列系统

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摘要

This paper proposes a microphone array system that integrates beamforming, feature enhancement, and highly accurate noise feature model estimation based on spectral masking. Previously proposed methods for combining beamformers and single-channel post-filters estimate noise power spectra or noise features based only on spatial information acquired from multiple microphones. These methods suffer from low noise estimation accuracy when the available microphones are limited or when there are array calibration or steering vector estimation errors. By contrast, the proposed method estimates a noise feature model accurately in a highly adaptive way by capitalizing on both spatial information and the characteristics of speech. Specifically, the method leverages an inter-microphone phase difference model, a clean feature model, and a harmonicitybased spectral mask model for the accurate estimation of spectral masks, each of which indicates the presence or absence of speech at a particular frequency bin. The estimated spectral masks are used to obtain the time-varying noise feature model. Results of a digit recognition experiment prove that the proposed system significantly outperforms an existing microphone array system combining a beamformer and a post-filter.
机译:本文提出了一种麦克风阵列系统,该系统集成了基于频谱掩蔽的波束形成,特征增强和高精度噪声特征模型估计。先前提出的用于组合波束形成器和单通道后置滤波器的方法仅基于从多个麦克风获取的空间信息来估计噪声功率谱或噪声特征。当可用麦克风受到限制或存在阵列校准或操纵矢量估计错误时,这些方法的噪声估计准确性较低。相比之下,所提出的方法通过充分利用空间信息和语音特征,以高度自适应的方式准确估计噪声特征模型。具体地,该方法利用麦克风间相位差模型,纯净特征模型和基于谐波的频谱掩模模型来精确估计频谱掩模,频谱掩模中的每一个指示在特定频率仓处语音的存在或不存在。估计的频谱掩码用于获得时变噪声特征模型。数字识别实验的结果证明,所提出的系统明显优于结合了波束形成器和后置滤波器的现有麦克风阵列系统。

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