基于Householder变换的声学降噪理论研究

Noise reduction consists of recovering clean speech signal from the noisy signals, which are corrupted by unwanted additive noise. It can find numerous applications in various fields of speech processing, such as teleconferencing, immersive communications, hearing aids systems and so on. As the rapid development of the acoustic industry, it has a high demand for noise reduction algorithms with excellent performance, high robustness and practical value. However, the performance of traditional noise reduction methods too much depends on the noise estimation accuracy and voice activity detection process. As a result, we intend to establish a comprehensive novel theory framework of noise reduction based on Householder transformation. Householder transformation converts the noisy signal coefficients into two components: speech dominated and noise dominated component, and then optimal filters can be obtained. One of the major advantages of this approach as compared to many traditional noise reduction techniques is that it does not need a noise estimator or a voice activity detector. Our method can also be combined with some other acoustic signal processing algorithms based on Householder transformation to form a complete theoretical system, which will be helpful for the innovation and development of domestic acoustic industry.

声学降噪，就是从受噪声污染的语音信号中提取出纯净语音的过程，它被广泛应用于远程会议、临境通讯、助听设备等领域。随着声学产业的飞速发展，急需与之相匹配的性能优异、鲁棒性高、实用性强的声学降噪算法。但是，传统声学降噪理论过于依赖噪声估计精度和语音活动检测过程，限制了降噪算法的使用性能。在这样的背景下，本项目拟建立一套完善的基于Householder变换的声学降噪理论方案。利用Householder变换，我们将信号转换为两个部分：第一部分主要由纯净信号构成，第二部分主要由噪声构成，利用分解后的结果进一步推导相应滤波器。与传统声学降噪过程比较，本项目所提出的方法不需要进行噪声估计或者语音活动检测，为声学降噪理论的发展提供了新的思路。另外该方法还可与基于Householder变换的其他声信号处理算法相结合，形成一套完备的理论体系。这些研究结果将有力推动国内声学产业的创新和发展。