Amplitude demodulation is a classical operation used in signal processing. For a long time, its effective applications in practice have been limited to narrowband signals. In this work, we generalize amplitude demodulation to wideband signals. We pose demodulation as a recovery problem of an oversampled corrupted signal and introduce special iterative schemes belonging to the family of alternating projection algorithms to solve it. Sensibly chosen structural assumptions on the demodulation outputs allow us to reveal the high inferential accuracy of the method over a rich set of relevant signals. This new approach surpasses current state-of-the-art demodulation techniques apt to wideband signals in computational efficiency by up to many orders of magnitude with no sacrifice in quality. Such performance opens the door for applications of the amplitude demodulation procedure in new contexts. In particular, the new method makes online and large-scale offline data processing feasible, including the calculation of modulator-carrier pairs in higher dimensions and poor sampling conditions, independent of the signal bandwidth. We illustrate the utility and specifics of applications of the new method in practice by using synthetic and natural speech signals.
翻译:信号处理过程中使用的典型操作是振幅降幅降幅。 长期以来, 其有效应用实际上仅限于窄带信号。 在这项工作中, 我们将振幅降幅推广到宽带信号。 我们将降幅降幅作为过度采样的腐败信号的恢复问题, 并引入属于交替投影算法家族的特殊迭代计划来解决它。 降幅输出输出的明智选择的结构假设使我们得以显示该方法相对于大量相关信号的高度推断准确性。 这种新做法超过了目前能够以不牺牲质量的方式在计算效率方面达到许多数量级的宽带信号的先进降幅降幅技术。 这种性能为在新的环境下应用振幅降幅降幅程序打开了大门。 特别是, 新方法使得在线和大型离线数据处理变得可行, 包括计算在较高尺寸和不依赖信号带宽的取样条件下的摩托式- 载机配对。 我们用合成和自然语音信号来说明新方法的实际应用的实用性和具体性。