Most of the work we do in signal processing these days is data driven. The shift from the more traditional and model-driven approaches to those that are data driven has also underlined the importance of explainability of our solutions. Because most traditional signal processing approaches start with a number of modeling assumptions, they are comprehensible by the very nature of their construction. However, this is not necessarily the case when we choose to rely more heavily on the data and minimize modeling assumptions.

中文翻译：

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可解释性、再现性和可复制性 [来自客座编辑]

如今，我们在信号处理中所做的大部分工作都是数据驱动的。从更传统的和模型驱动的方法到数据驱动的方法的转变也强调了我们解决方案可解释性的重要性。因为大多数传统的信号处理方法都是从许多建模假设开始的，所以它们的构造本质是可以理解的。然而，当我们选择更多地依赖数据并最小化建模假设时，情况并非如此。