This work introduces a wavelet neural network to learn a filter-bank specialized to fit non-stationary signals and improve interpretability and performance for digital signal processing. The network uses a wavelet transform as the first layer of a neural network where the convolution is a parameterized function of the complex Morlet wavelet. Experimental results, on both simplified data and atmospheric gravity waves, show the network is quick to converge, generalizes well on noisy data, and outperforms standard network architectures.
翻译:这项工作引入了波子神经网络, 以学习一个专门适合非静止信号的过滤库, 并改进数字信号处理的可解释性和性。 网络使用波子变换作为神经网络的第一层, 使波子变换成为复杂的摩尔子波子的参数函数。 在简化数据和大气重力波上, 实验结果显示网络很快会汇合, 广泛使用吵闹的数据, 并且超过了标准的网络结构。