论文标题
通过后滤光到临时传感器网络中的语音和音频编码来增强
Enhancement by postfiltering for speech and audio coding in ad-hoc sensor networks
论文作者
论文摘要
无线声学传感器网络的增强算法〜（WASNS）是必不可少的，随着可用性的增加和使用麦克风连接的设备的使用量增加。常规的空间滤波方法以增强WASN的增强量量化噪声，并具有加性高斯分布，这限制了较低比特率在较低比特率下量化噪声的非线性性质引起的性能。在这项工作中，我们提出了一个基于贝叶斯统计数据的后过滤器来增强功能，以获得多个Vice信号估计，该估算明确地对量化噪声进行了建模。我们使用PSNR，PESQ和MUSHRA分数进行的实验表明，所提出的后滤波器可用于增强临时传感器网络中的信号质量。
Enhancement algorithms for wireless acoustics sensor networks~(WASNs) are indispensable with the increasing availability and usage of connected devices with microphones. Conventional spatial filtering approaches for enhancement in WASNs approximate quantization noise with an additive Gaussian distribution, which limits performance due to the non-linear nature of quantization noise at lower bitrates. In this work, we propose a postfilter for enhancement based on Bayesian statistics to obtain a multidevice signal estimate, which explicitly models the quantization noise. Our experiments using PSNR, PESQ and MUSHRA scores demonstrate that the proposed postfilter can be used to enhance signal quality in ad-hoc sensor networks.
