论文标题
最大可行的子系统算法，用于恢复压缩感知的语音
Maximum Feasible Subsystem Algorithms for Recovery of Compressively Sensed Speech
论文作者
论文摘要
信号压缩的目标是减小输入信号的大小，而不会显着恢复信号的质量损失。实现这一目标的一种方法是应用压缩感应的原则，但是对于不够稀疏的现实信号（例如语音）而言，这并不是特别成功。我们基于解决方案的最大可行子系统问题（最大FS）的解决方案提出了三种新算法，这些算法在压缩语音信号的恢复中改善了最新的状态：可以以更高的质量成功恢复更高的压缩信号。与使用传统压缩感应恢复算法获得的新恢复算法相比，新的恢复算法可提供更稀疏的溶液。当通过在TIMIT语音数据库中恢复压缩感知的语音信号进行测试时，恢复的语音比使用传统的压缩传感恢复算法恢复的语音的感知质量更好。
The goal in signal compression is to reduce the size of the input signal without a significant loss in the quality of the recovered signal. One way to achieve this goal is to apply the principles of compressive sensing, but this has not been particularly successful for real-world signals that are insufficiently sparse, such as speech. We present three new algorithms based on solutions for the maximum feasible subsystem problem (MAX FS) that improve on the state of the art in recovery of compressed speech signals: more highly compressed signals can be successfully recovered with greater quality. The new recovery algorithms deliver sparser solutions when compared with those obtained using traditional compressive sensing recovery algorithms. When tested by recovering compressively sensed speech signals in the TIMIT speech database, the recovered speech has better perceptual quality than speech recovered using traditional compressive sensing recovery algorithms.
