标题： 时间频率图像表示和深度学习技术在信号分类中的应用案例 显示英文标题

标题： Use Cases for Time-Frequency Image Representations and Deep Learning Techniques for Improved Signal Classification

摘要： 时频图像（TFIs）提供了信号的联合时频表示，已成为分析、表征和处理非平稳信号的有效工具。 深度学习（DL）技术已成为信号分类的多功能工具，能够从原始数据中自动提取相关特征。 在本文中，我们展示了两个关于时频变换和深度学习技术在信号分类中的应用案例，其中信号首先被预处理并转换为TFIs，然后通过深度学习神经网络和分类算法提取其特征。 所使用的具体方法和算法可能因特定应用而异，因此研究了不同的创建TFI的方法；包括短时傅里叶变换（STFT）、基于傅里叶的同步挤压变换（FSST）、维格纳-维尔分布（WVD）、平滑伪维格纳分布（SPWD）、乔伊-威廉姆斯分布（CWD）和连续小波变换（CWT）。 评估了各种深度学习和卷积神经网络（CNN）模型如ResNet-50、ShuffleNet和Squeezenet在不同应用中的分类准确性，并将其结果与传统机器学习和集成方法如多层感知器（MLP）、支持向量机（SVM）、随机森林（RF）、决策树（DT）和XGboost的结果进行比较。 本研究的结果表明，通过利用TFI和深度学习模型的综合力量，可以显著提高信号分类的准确性。 这些进展已在雷达信号分类、电力系统稳定性分析、语音和音乐识别以及生物医学信号表征等多个领域找到了实际应用。 显示英文摘要

摘要： Time-frequency images (TFIs) provide a joint time-frequency representation of a signal and have become an effective tool for analyzing, characterizing, and processing non-stationary signals. Deep learning (DL) techniques have become versatile for signal classification, enabling the automatic extraction of relevant features from raw data. In this paper, we present two use cases on the time-frequency transformation and deep learning techniques for signal classification, where signals are first pre-processed and transformed into TFIs, and their features are then extracted through deep learning neural networks and classification algorithms. The specific methods and algorithms used may vary depending on the particular application, therefore different methods for creating TFIs; the Short-Time Fourier Transform (STFT), Fourier-based Synchrosqueezing Transform (FSST), Wigner Ville distribution (WVD), Smoothed Pseudo-Wigner distribution (SPWD), Choi-Williams distribution (CWD), and Continuous Wavelet Transform (CWT) are investigated. The performance of various deep learning, and convolutional neural network (CNN) models such as ResNet-50, ShuffleNet, and Squeezenet are evaluated for their accuracy of classification in different applications and the results are compared with the results of the conventional machine learning and ensemble methods such as Multilayer Perceptrons (MLP), Support Vector Machine (SVM), Random Forest (RF), Decision Tree (DT), and XGboost. The results of this research demonstrate that significant improvements in signal classification accuracy can be achieved by leveraging the combined power of TFIs, and deep learning models. These advances have found practical applications in a wide range of fields, including radar signal classification, stability analysis of power systems, speech and music recognition, and biomedical signal characterization.