[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-83973-en":3,"doc-seo-83973-105":29,"detail-sidebar-cat-0-en-105":90},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":13,"seo_description":14,"update_tm":27,"read_time":28},83973,7971461740886,"Theodore","https://ap-avatar.wpscdn.com/davatar_3d24733baf745e90a7e4bdd5f77d97b2",8,"Research & Report","Distributed Multichannel Wiener Filtering for Topology-Unconstrained Wireless Acoustic Sensor Networks","This paper presents topology-independent distributed multichannel Wiener filtering (TIdMWF) for topology-unconstrained wireless acoustic sensor networks, enabling node-specific signal estimation with centralized multichannel Wiener filter performance. Each node computes the centralized solution by exchanging only low-dimensional fused signals, avoiding iterative estimation used by related methods such as TI-DANSE. Optimality is proven under specific observability conditions, and theoretical plus numerical results verify single-pass centralized performance. Latency versus pruned-tree depth, computational complexity, and robustness in reverberant-room simulations are analyzed under estimated statistics, topology variations, and model deviations.","arXiv :2607 .05561v1 [ ee ss .AS] 6 Jul 2026  \nReceived XX Month, XXXX; revised XX Month, XXXX; accepted XX Month, XXXX; Date of publication XX Month, XXXX; date of  \ncurrent version XX Month, XXXX.  \nDigital Object Identifier 10.1109/XXXX.2022.1234567  \nDistributed Multichannel Wiener Filtering for Topology-Unconstrained Wireless Acoustic Sensor Networks  \nPaul Didier1 , Pourya Behmandpoor4 , Henri Gode2 , Toon van Waterschoot1 , Simon Doclo2,3 , Jrg Bitzer3 , and Marc Moonen1  \n1 STADIUS Center for Dynamical Systems, Signal Processing, and Data Analytics, Electrical Engineering Department (ESAT), KU Leuven, Leuven, Belgium  \n2 Signal Processing Group, Department of Medical Physics, and with the Acoustics and Cluster of Excellence Hearing4all, Carl von Ossietzky Universitt Oldenburg, Oldenburg, Germany  \n3 Fraunhofer IDMT, Project Group Hearing, Speech and Audio Technology, Oldenburg, Germany  \n4 Department of Electronics and Informatics, Vrije Universiteit Brussel, B-1050 Brussels, Belgium Corresponding author: Paul Didier (email: [phmdidier@proton.me](phmdidier@proton.me)).  \nThis research work was carried out in the frame of Research Council KU Leuven project C14-21-0075 ”A holistic approach to the design of integrated and distributed digital signal processing algorithms for audio and speech communication devices” and the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie Grant Agreement No. 956369: “Service-Oriented Ubiquitous Network-Driven Sound—SOUNDS”. This paper reflects only the authors’ views and the Union is not liable for any use that may be made of the contained information. The scientific responsibility is assumed by the authors.  \nABSTRACT This paper introduces the topology-independent distributed multichannel Wiener filter (TIdMWF), a novel algorithm for distributed node-specific signal estimation in wireless acoustic sensor networks (WASNs) with unconstrained topologies. The TI-dMWF enables each node in the network to compute its centralized multichannel Wiener filter solution by exchanging only low-dimensional fused signals, without requiring iterative estimation, unlike state-of-the-art approaches such as the topologyindependent distributed adaptive node-specific signal estimation (TI-DANSE) algorithm. The TI-dMWF is proven optimal when each source is observed by either all nodes or only one node. Theoretical analysis and numerical simulations confirm that it achieves centralized estimation performance in a single run. Its latency as a function of the pruned-tree depth and its computational complexity are also analyzed. Its robustness is assessed in reverberant-room simulations under estimated second-order statistics, various network topologies, and deviations from the assumed observability model.  \nINDEX TERMS Distributed signal processing, ad-hoc wireless acoustic sensor networks, multichannel Wiener filter, dimensionality reduction, distributed noise reduction  \nI. Introduction  \nIN recent years, devices capable of recording,  \ning, and exchanging audio data wirelessly have  \nprocessbecome  \nubiquitous, enabling the creation of wireless acoustic sensor networks (WASNs), which have gained attention for applications such as environmental monitoring and smart homes [1],[2] . Within a WASN, devices (nodes) equipped with one or more microphones (sensors), such as smartphones, laptops, or hearing aids, collaborate to perform audio signal processing tasks, e.g., noise reduction [3]–[5], dereverberation [6],[7], or sound source localization [8], [9] . Compared to traditional fixed microphone arrays, WASNs offer greater spatial coverage, deployment flexibility, and robustness to  \nnode failures [1], but operate under constraints such as communication cost and potentially dynamic topologies, making the design of distributed algorithms that respect these constraints a key challenge.  \nThis paper addresses node-specific signal estimation ina WASN, where each node estima","cbCaiphwnwlibb8S","https://ap.wps.com/l/cbCaiphwnwlibb8S","pdf",558014,1,9,"English","en",105,"# Introduction\n## Node-specific signal estimation in WASNs\n## Centralized MWF and communication constraints\n## Distributed multichannel Wiener filtering and topology independence\n## Proposed TIdMWF approach and contributions","[{\"question\":\"What is the topology-independent distributed multichannel Wiener filter (TIdMWF) designed to do?\",\"answer\":\"TIdMWF performs distributed, node-specific signal estimation in wireless acoustic sensor networks with unconstrained (time-varying) topologies, achieving centralized multichannel Wiener filter performance.\"},{\"question\":\"How does TIdMWF reduce communication compared with centralized multichannel Wiener filtering?\",\"answer\":\"Each node exchanges only low-dimensional fused signals rather than all sensor signals, enabling the centralized solution to be computed distributively.\"},{\"question\":\"What do the results say about latency, complexity, and robustness of TIdMWF?\",\"answer\":\"The paper analyzes latency as a function of pruned-tree depth and evaluates computational complexity. Robustness is assessed through reverberant-room simulations under estimated second-order statistics, varying topologies, and deviations from the assumed observability model.\"}]",1784191789,23,{"code":4,"msg":30,"data":31},"ok",{"site_id":24,"language":23,"slug":32,"title":13,"keywords":33,"description":14,"schema_data":34,"social_meta":85,"head_meta":87,"extra_data":89,"updated_unix":27},"distributed-multichannel-wiener-filtering-for-topology-unconstrained-wireless-acoustic-sensor-networks","",{"@graph":35,"@context":84},[36,53,67],{"@type":37,"itemListElement":38},"BreadcrumbList",[39,43,47,50],{"item":40,"name":41,"@type":42,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":44,"name":45,"@type":42,"position":46},"https://docshare.wps.com/document/","Document",2,{"item":48,"name":12,"@type":42,"position":49},"https://docshare.wps.com/document/research-report/",3,{"item":51,"name":13,"@type":42,"position":52},"https://docshare.wps.com/document/distributed-multichannel-wiener-filtering-for-topology-unconstrained-wireless-acoustic-sensor-networks/83973/",4,{"url":51,"name":13,"@type":54,"author":55,"headline":13,"publisher":57,"fileFormat":60,"inLanguage":23,"description":14,"dateModified":61,"datePublished":61,"encodingFormat":60,"isAccessibleForFree":62,"interactionStatistic":63},"DigitalDocument",{"name":9,"@type":56},"Person",{"url":40,"name":58,"@type":59},"DocShare","Organization","application/pdf","2026-07-16",true,{"@type":64,"interactionType":65,"userInteractionCount":4},"InteractionCounter",{"@type":66},"ViewAction",{"@type":68,"mainEntity":69},"FAQPage",[70,76,80],{"name":71,"@type":72,"acceptedAnswer":73},"What is the topology-independent distributed multichannel Wiener filter (TIdMWF) designed to do?","Question",{"text":74,"@type":75},"TIdMWF performs distributed, node-specific signal estimation in wireless acoustic sensor networks with unconstrained (time-varying) topologies, achieving centralized multichannel Wiener filter performance.","Answer",{"name":77,"@type":72,"acceptedAnswer":78},"How does TIdMWF reduce communication compared with centralized multichannel Wiener filtering?",{"text":79,"@type":75},"Each node exchanges only low-dimensional fused signals rather than all sensor signals, enabling the centralized solution to be computed distributively.",{"name":81,"@type":72,"acceptedAnswer":82},"What do the results say about latency, complexity, and robustness of TIdMWF?",{"text":83,"@type":75},"The paper analyzes latency as a function of pruned-tree depth and evaluates computational complexity. 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