In many applications, signal denoising is often the first pre-processing step before any subsequent analysis or learning task. In this paper, we propose to apply a deep learning denoising model inspired by a signal processing, a learnable version of wavelet packet transform. The proposed algorithm has signficant learning capabilities with few interpretable parameters and has an intuitive initialisation. We propose a post-learning modification of the parameters to adapt the denoising to different noise levels. We evaluate the performance of the proposed methodology on two case studies and compare it to other state of the art approaches, including wavelet schrinkage denoising, convolutional neural network, autoencoder and U-net deep models. The first case study is based on designed functions that have typically been used to study denoising properties of the algorithms. The second case study is an audio background removal task. We demonstrate how the proposed algorithm relates to the universality of signal processing methods and the learning capabilities of deep learning approaches. In particular, we evaluate the obtained denoising performances on structured noisy signals inside and outside the classes used for training. In addition to having good performance in denoising signals inside and outside to the training class, our method shows to be particularly robust when different noise levels, noise types and artifacts are added.
翻译:在许多应用中,信号脱去往往是在随后进行任何分析或学习任务之前的第一个预处理步骤。 在本文中,我们提议应用一个由信号处理、可学习的波盘包变换版本所启发的深层次学习脱去模式。提议的算法具有标志性学习能力,没有多少可解释的参数,并且具有直觉的初始化功能。我们提议对参数进行学习后修改,以调整脱去到不同的噪音水平。我们评价两个案例研究的拟议方法的绩效,并将它与其他艺术方法的状态进行比较,包括由信号处理、传动神经网络、自动编码器和U-net深层模型所启发的深层次学习模式所启发的深层次学去模式。第一个案例研究所依据的是通常用于研究算法解开特性的设计功能。第二个案例研究是一项删除声音的背景任务。我们演示拟议的算法如何与信号处理方法的普遍性和深层次学习能力相联系。我们特别评价在用于培训的课堂内外结构噪音信号结构化方面所取得的脱色性表现。此外,除了在课堂内进行稳健的噪音测试之外,还特别展示了在外的噪音等级上的优良性能。