As network supporting devices and sensors in the Internet of Things are leaping forward, countless real-world data will be generated for human intelligent applications. Speech sensor networks, an important part of the Internet of Things, have numerous application needs. Indeed, the sensor data can further help intelligent applications to provide higher quality services, whereas this data may involve considerable noise data. Accordingly, speech signal processing method should be urgently implemented to acquire low-noise and effective speech data. Blind source separation and enhancement technique refer to one of the representative methods. However, in the unsupervised complex environment, in the only presence of a single-channel signal, many technical challenges are imposed on achieving single-channel and multiperson mixed speech separation. For this reason, this study develops an unsupervised speech separation method CNMF+JADE, i.e., a hybrid method combined with Convolutional Non-Negative Matrix Factorization and Joint Approximative Diagonalization of Eigenmatrix. Moreover, an adaptive wavelet transform-based speech enhancement technique is proposed, capable of adaptively and effectively enhancing the separated speech signal. The proposed method is aimed at yielding a general and efficient speech processing algorithm for the data acquired by speech sensors. As revealed from the experimental results, in the TIMIT speech sources, the proposed method can effectively extract the target speaker from the mixed speech with a tiny training sample. The algorithm is highly general and robust, capable of technically supporting the processing of speech signal acquired by most speech sensors.

中文翻译：

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一种改进的无监督单通道语音分离算法，用于处理语音传感器信号

随着物联网中网络支持设备和传感器的飞跃发展，将为人类智能应用生成无数的现实世界数据。语音传感器网络是物联网的重要组成部分，具有众多的应用需求。实际上，传感器数据可以进一步帮助智能应用程序提供更高质量的服务，而该数据可能涉及大量的噪声数据。因此，应紧急实施语音信号处理方法以获取低噪声且有效的语音数据。盲源分离和增强技术指的是代表性方法之一。然而，在无人监督的复杂环境中，在仅存在单通道信号的情况下，实现单通道和多人混合语音分离存在许多技术挑战。因此，本研究开发了一种无监督语音分离方法CNMF + JADE，即结合了卷积非负矩阵分解和特征矩阵联合近似对角线的混合方法。此外，提出了一种基于自适应小波变换的语音增强技术，能够自适应有效地增强分离后的语音信号。所提出的方法旨在针对由语音传感器获取的数据产生一种通用且有效的语音处理算法。从实验结果可以看出，在TIMIT语音源中，该方法可以有效地从混合语音中提取目标说话者，并且只需很小的训练样本即可。该算法具有很高的通用性和鲁棒性，能够在技术上支持大多数语音传感器获取的语音信号的处理。