论文标题
使用可学习的小波数据包转换的稳健时间序列denoising
Robust Time Series Denoising with Learnable Wavelet Packet Transform
论文作者
论文摘要
信号denoising是许多应用程序的关键预处理步骤，因为学习任务的性能与输入数据的质量密切相关。在本文中，我们应用了基于信号处理的深神经网络体系结构，这是小波包装转换的可学习扩展。作为主要优势，该模型几乎没有参数，直观的初始化和强大的学习能力。此外，我们表明，在训练步骤之后，可以轻松修改模型的参数，以量身定制不同的噪声强度。进行了两项案例研究，以将该模型与最新技术和常用的非授权程序进行比较。第一个实验使用标准信号来研究算法的剥落性质。第二个实验是一个真正的应用程序，其目的是消除音频背景噪音。我们表明，可学习的小波数据包变换具有深度学习方法的学习能力，同时保持标准信号处理方法的鲁棒性。更具体地说，我们证明我们的方法在训练步骤中使用的信号类别保持出色的信号类别表演。此外，当考虑了不同的噪声强度，噪声品种和伪影时，发现可学习的小波数据包变换是可靠的。
Signal denoising is a key preprocessing step for many applications, as the performance of a learning task is closely related to the quality of the input data. In this paper, we apply a signal processing based deep neural network architecture, a learnable extension of the wavelet packet transform. As main advantages, this model has few parameters, an intuitive initialization and strong learning capabilities. Moreover, we show that it is possible to easily modify the parameters of the model after the training step to tailor to different noise intensities. Two case studies are conducted to compare this model with the state of the art and commonly used denoising procedures. The first experiment uses standard signals to study denoising properties of the algorithms. The second experiment is a real application with the objective to remove audio background noises. We show that the learnable wavelet packet transform has the learning capabilities of deep learning methods while maintaining the robustness of standard signal processing approaches. More specifically, we demonstrate that our approach maintains excellent denoising performances on signal classes separate from those used during the training step. Moreover, the learnable wavelet packet transform was found to be robust when different noise intensities, noise varieties and artifacts are considered.
