Abstract Transient interferences such as keystrokes, mouse clicks and hammering pose a significant challenge in the single channel speech enhancement due to their abrupt and non-continuous nature. Traditional noise suppression algorithms and even many non-stationary noise reduction algorithms do not adequately suppress transient interference. Therefore, in this work, we propose a semi-supervised single channel transient noise suppression method to effectively suppress the transient interference without significant audible distortion. The proposed algorithm consists of training and testing stages. In the training stage, the proposed technique first uses the optimally modified-log spectral amplitude (OMLSA) estimator to estimate the transient noise from the noisy speech signal. After that, we eliminate the residual speech components from the estimated noise obtained from OMLSA based on the correlation coefficient, by taking correlation between the estimated noise with the available clean speech data from the dataset passed through the voice activity detector for silence zones removal. Afterwards, we use this noise for training the noise dictionary in sparse non-negative matrix factorization. Clean speech data is used for speech dictionary training. In the enhancement stage, the dictionaries are fixed and concatenated, to obtain the corresponding activation matrices. The clean speech dictionary and the corresponding weight matrix are used to reconstruct the estimated speech. The experimental results reveal that the proposed algorithm provided better performance compared to other existing algorithms in the speech quality evaluation metrics.

中文翻译：

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使用 OMLSA 和 SNMF 算法的半监督瞬态噪声抑制

摘要 瞬态干扰，如击键、鼠标点击和锤击，由于其突然和非连续性，对单通道语音增强提出了重大挑战。传统的降噪算法，甚至很多非平稳降噪算法都不能充分抑制瞬态干扰。因此，在这项工作中，我们提出了一种半监督的单通道瞬态噪声抑制方法，以有效抑制瞬态干扰而不会产生明显的可听失真。所提出的算法包括训练和测试阶段。在训练阶段，所提出的技术首先使用优化修改对数谱幅度 (OMLSA) 估计器从带噪语音信号中估计瞬态噪声。之后，我们根据相关系数从 OMLSA 获得的估计噪声中消除残余语音分量，方法是获取估计噪声与来自数据集的可用干净语音数据之间的相关性，该数据集通过语音活动检测器进行静音区去除。之后，我们使用此噪声在稀疏非负矩阵分解中训练噪声字典。干净的语音数据用于语音词典训练。在增强阶段，将字典固定并连接起来，以获得相应的激活矩阵。干净的语音字典和相应的权重矩阵用于重建估计的语音。