论文标题
可学习的小波数据包转换用于数据适应的频谱图
Learnable Wavelet Packet Transform for Data-Adapted Spectrograms
论文作者
论文摘要
捕获有关复杂系统状况的高频数据，例如通过声学监测，越来越普遍。这样的高频信号通常包含在不同时间尺度和不同类型的循环行为的时间依赖性。处理此类信号需要仔细的功能工程，尤其是提取有意义的时频功能。这可能是耗时的，并且性能通常取决于参数的选择。为了解决这些局限性，我们为可学习的小波数据包变换提出了一个深度学习框架，使能够从数据自动学习功能，并优化它们，以相对于定义的目标函数。学习的功能可以表示为频谱图，其中包含数据集的重要时频信息。我们通过评估其改进的光谱泄漏并将其应用于声学监测的异常检测任务来评估所提出方法的性质和性能。
Capturing high-frequency data concerning the condition of complex systems, e.g. by acoustic monitoring, has become increasingly prevalent. Such high-frequency signals typically contain time dependencies ranging over different time scales and different types of cyclic behaviors. Processing such signals requires careful feature engineering, particularly the extraction of meaningful time-frequency features. This can be time-consuming and the performance is often dependent on the choice of parameters. To address these limitations, we propose a deep learning framework for learnable wavelet packet transforms, enabling to learn features automatically from data and optimise them with respect to the defined objective function. The learned features can be represented as a spectrogram, containing the important time-frequency information of the dataset. We evaluate the properties and performance of the proposed approach by evaluating its improved spectral leakage and by applying it to an anomaly detection task for acoustic monitoring.
