{"title":"用于频率检测的系统信息神经网络","authors":"Sunyoung Ko;Myoungin Shin;Geunhwan Kim;Youngmin Choo","doi":"10.1109/LSP.2024.3483036","DOIUrl":null,"url":null,"abstract":"We contrive a deep learning-based frequency analysis scheme called system-informed neural network (SINN) by considering the corresponding linear system model. SINN adopts the adaptive learned iterative soft shrinkage algorithm as the NN architecture and includes the system model in loss function. It has good generalization with fast processing time and finds a solution that satisfies the system model as a physics-informed neural network. To further improve SINN, multiple measurements are exploited by assuming the existence of common frequency components over the measurements. SINN is examined using simulated acoustic data, and the performance is compared to Fourier transform and sparse Bayesian learning (SBL) in terms of the detection/false alarm rate and mean squared error. SINN exhibits clear frequency components in in-situ data tests, as in SBL, by reducing noise effectively. Finally, SINN is applied to noisy passive sonar signals, which include 43 frequency components, and many are recovered.","PeriodicalId":13154,"journal":{"name":"IEEE Signal Processing Letters","volume":null,"pages":null},"PeriodicalIF":3.2000,"publicationDate":"2024-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"System-Informed Neural Network for Frequency Detection\",\"authors\":\"Sunyoung Ko;Myoungin Shin;Geunhwan Kim;Youngmin Choo\",\"doi\":\"10.1109/LSP.2024.3483036\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"We contrive a deep learning-based frequency analysis scheme called system-informed neural network (SINN) by considering the corresponding linear system model. SINN adopts the adaptive learned iterative soft shrinkage algorithm as the NN architecture and includes the system model in loss function. It has good generalization with fast processing time and finds a solution that satisfies the system model as a physics-informed neural network. To further improve SINN, multiple measurements are exploited by assuming the existence of common frequency components over the measurements. SINN is examined using simulated acoustic data, and the performance is compared to Fourier transform and sparse Bayesian learning (SBL) in terms of the detection/false alarm rate and mean squared error. SINN exhibits clear frequency components in in-situ data tests, as in SBL, by reducing noise effectively. Finally, SINN is applied to noisy passive sonar signals, which include 43 frequency components, and many are recovered.\",\"PeriodicalId\":13154,\"journal\":{\"name\":\"IEEE Signal Processing Letters\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":3.2000,\"publicationDate\":\"2024-10-17\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"IEEE Signal Processing Letters\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://ieeexplore.ieee.org/document/10720822/\",\"RegionNum\":2,\"RegionCategory\":\"工程技术\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"ENGINEERING, ELECTRICAL & ELECTRONIC\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Signal Processing Letters","FirstCategoryId":"5","ListUrlMain":"https://ieeexplore.ieee.org/document/10720822/","RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ENGINEERING, ELECTRICAL & ELECTRONIC","Score":null,"Total":0}
引用次数: 0
摘要
考虑到相应的线性系统模型,我们设计了一种基于深度学习的频率分析方案,称为系统信息神经网络(SINN)。SINN 采用自适应学习迭代软收缩算法作为神经网络架构,并在损失函数中包含系统模型。作为一种物理信息神经网络,它具有良好的泛化能力和快速的处理时间,并能找到满足系统模型的解。为了进一步改进 SINN,通过假设测量中存在共同的频率成分,利用了多重测量。利用模拟声学数据对 SINN 进行了检验,并在检测/误报率和均方误差方面与傅立叶变换和稀疏贝叶斯学习(SBL)进行了性能比较。与 SBL 一样,SINN 通过有效降低噪声,在现场数据测试中表现出清晰的频率成分。最后,将 SINN 应用于包含 43 个频率成分的高噪声被动声纳信号,其中许多频率成分得到了恢复。
System-Informed Neural Network for Frequency Detection
We contrive a deep learning-based frequency analysis scheme called system-informed neural network (SINN) by considering the corresponding linear system model. SINN adopts the adaptive learned iterative soft shrinkage algorithm as the NN architecture and includes the system model in loss function. It has good generalization with fast processing time and finds a solution that satisfies the system model as a physics-informed neural network. To further improve SINN, multiple measurements are exploited by assuming the existence of common frequency components over the measurements. SINN is examined using simulated acoustic data, and the performance is compared to Fourier transform and sparse Bayesian learning (SBL) in terms of the detection/false alarm rate and mean squared error. SINN exhibits clear frequency components in in-situ data tests, as in SBL, by reducing noise effectively. Finally, SINN is applied to noisy passive sonar signals, which include 43 frequency components, and many are recovered.
期刊介绍:
The IEEE Signal Processing Letters is a monthly, archival publication designed to provide rapid dissemination of original, cutting-edge ideas and timely, significant contributions in signal, image, speech, language and audio processing. Papers published in the Letters can be presented within one year of their appearance in signal processing conferences such as ICASSP, GlobalSIP and ICIP, and also in several workshop organized by the Signal Processing Society.