{"title":"GFANC-RL:基于强化学习的生成式固定滤波主动噪声控制","authors":"","doi":"10.1016/j.neunet.2024.106687","DOIUrl":null,"url":null,"abstract":"<div><div>The recent Generative Fixed-filter Active Noise Control (GFANC) method achieves a good trade-off between noise reduction performance and system stability. However, labelling noise data for training the Convolutional Neural Network (CNN) in GFANC is typically resource-consuming. Even worse, labelling errors will degrade the CNN’s filter-generation accuracy. Therefore, this paper proposes a novel Reinforcement Learning-based GFANC (GFANC-RL) approach that omits the labelling process by leveraging the exploring property of Reinforcement Learning (RL). The CNN’s parameters are automatically updated through the interaction between the RL agent and the environment. Moreover, the RL algorithm solves the non-differentiability issue caused by using binary combination weights in GFANC. Simulation results demonstrate the effectiveness and transferability of the GFANC-RL method in handling real-recorded noises across different acoustic paths.<span><span><sup>2</sup></span></span></div></div>","PeriodicalId":49763,"journal":{"name":"Neural Networks","volume":null,"pages":null},"PeriodicalIF":6.0000,"publicationDate":"2024-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"GFANC-RL: Reinforcement Learning-based Generative Fixed-filter Active Noise Control\",\"authors\":\"\",\"doi\":\"10.1016/j.neunet.2024.106687\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>The recent Generative Fixed-filter Active Noise Control (GFANC) method achieves a good trade-off between noise reduction performance and system stability. However, labelling noise data for training the Convolutional Neural Network (CNN) in GFANC is typically resource-consuming. Even worse, labelling errors will degrade the CNN’s filter-generation accuracy. Therefore, this paper proposes a novel Reinforcement Learning-based GFANC (GFANC-RL) approach that omits the labelling process by leveraging the exploring property of Reinforcement Learning (RL). The CNN’s parameters are automatically updated through the interaction between the RL agent and the environment. Moreover, the RL algorithm solves the non-differentiability issue caused by using binary combination weights in GFANC. Simulation results demonstrate the effectiveness and transferability of the GFANC-RL method in handling real-recorded noises across different acoustic paths.<span><span><sup>2</sup></span></span></div></div>\",\"PeriodicalId\":49763,\"journal\":{\"name\":\"Neural Networks\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":6.0000,\"publicationDate\":\"2024-09-05\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Neural Networks\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S0893608024006117\",\"RegionNum\":1,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Neural Networks","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0893608024006117","RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE","Score":null,"Total":0}
GFANC-RL: Reinforcement Learning-based Generative Fixed-filter Active Noise Control
The recent Generative Fixed-filter Active Noise Control (GFANC) method achieves a good trade-off between noise reduction performance and system stability. However, labelling noise data for training the Convolutional Neural Network (CNN) in GFANC is typically resource-consuming. Even worse, labelling errors will degrade the CNN’s filter-generation accuracy. Therefore, this paper proposes a novel Reinforcement Learning-based GFANC (GFANC-RL) approach that omits the labelling process by leveraging the exploring property of Reinforcement Learning (RL). The CNN’s parameters are automatically updated through the interaction between the RL agent and the environment. Moreover, the RL algorithm solves the non-differentiability issue caused by using binary combination weights in GFANC. Simulation results demonstrate the effectiveness and transferability of the GFANC-RL method in handling real-recorded noises across different acoustic paths.2
期刊介绍:
Neural Networks is a platform that aims to foster an international community of scholars and practitioners interested in neural networks, deep learning, and other approaches to artificial intelligence and machine learning. Our journal invites submissions covering various aspects of neural networks research, from computational neuroscience and cognitive modeling to mathematical analyses and engineering applications. By providing a forum for interdisciplinary discussions between biology and technology, we aim to encourage the development of biologically-inspired artificial intelligence.