This letter proposes a novel blind acoustic mask (BAM) designed to adaptively detect noise components and preserve target speech segments in time domain. A robust standard deviation estimator is applied to the non-stationary noisy speech to identify noise masking elements. The main contribution of the proposed solution is the use of this noise statistics to derive an adaptive information to define and select samples with lower noise proportion. Thus, preserving speech intelligibility. Additionally, no information of the target speech and noise signals statistics is previously required to this non-ideal mask. The BAM and three competitive methods, Ideal Binary Mask (IBM), Target Binary Mask (TBM), and Non-stationary Noise Estimation for Speech Enhancement (NNESE), are evaluated considering speech signals corrupted by three non-stationary acoustic noises and six values of signal-to-noise ratio (SNR). Results demonstrate that the BAM technique achieves intelligibility gains comparable to ideal masks while maintaining good speech quality.

中文翻译：

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提高非平稳噪声语音清晰度的盲自适应掩模

这封信提出了一种新颖的盲声掩码 (BAM)，旨在自适应地检测噪声分量并在时域中保留目标语音片段。稳健的标准偏差估计器应用于非平稳带噪语音以识别噪声掩蔽元素。所提议的解决方案的主要贡献是使用该噪声统计来导出自适应信息以定义和选择具有较低噪声比例的样本。因此，保持语音可懂度。此外，该非理想掩膜先前不需要目标语音和噪声信号统计的信息。BAM 和三种竞争方法，理想二进制掩码 (IBM)、目标二进制掩码 (TBM) 和语音增强的非平稳噪声估计 (NNESE)，考虑被三个非平稳声学噪声和六个信噪比 (SNR) 值破坏的语音信号进行评估。结果表明，BAM 技术在保持良好语音质量的同时实现了与理想掩码相当的可懂度增益。