[{"page":"1-1","citation":{"apa":"Wever, M. D., Tornede, A., Mohr, F., &#38; Hüllermeier, E. (2021). AutoML for Multi-Label Classification: Overview and Empirical Evaluation. <i>IEEE Transactions on Pattern Analysis and Machine Intelligence</i>, 1–1. <a href=\"https://doi.org/10.1109/tpami.2021.3051276\">https://doi.org/10.1109/tpami.2021.3051276</a>","short":"M.D. Wever, A. Tornede, F. Mohr, E. Hüllermeier, IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) 1–1.","bibtex":"@article{Wever_Tornede_Mohr_Hüllermeier_2021, title={AutoML for Multi-Label Classification: Overview and Empirical Evaluation}, DOI={<a href=\"https://doi.org/10.1109/tpami.2021.3051276\">10.1109/tpami.2021.3051276</a>}, journal={IEEE Transactions on Pattern Analysis and Machine Intelligence}, author={Wever, Marcel Dominik and Tornede, Alexander and Mohr, Felix and Hüllermeier, Eyke}, year={2021}, pages={1–1} }","mla":"Wever, Marcel Dominik, et al. “AutoML for Multi-Label Classification: Overview and Empirical Evaluation.” <i>IEEE Transactions on Pattern Analysis and Machine Intelligence</i>, 2021, pp. 1–1, doi:<a href=\"https://doi.org/10.1109/tpami.2021.3051276\">10.1109/tpami.2021.3051276</a>.","ama":"Wever MD, Tornede A, Mohr F, Hüllermeier E. AutoML for Multi-Label Classification: Overview and Empirical Evaluation. <i>IEEE Transactions on Pattern Analysis and Machine Intelligence</i>. Published online 2021:1-1. doi:<a href=\"https://doi.org/10.1109/tpami.2021.3051276\">10.1109/tpami.2021.3051276</a>","ieee":"M. D. Wever, A. Tornede, F. Mohr, and E. Hüllermeier, “AutoML for Multi-Label Classification: Overview and Empirical Evaluation,” <i>IEEE Transactions on Pattern Analysis and Machine Intelligence</i>, pp. 1–1, 2021, doi: <a href=\"https://doi.org/10.1109/tpami.2021.3051276\">10.1109/tpami.2021.3051276</a>.","chicago":"Wever, Marcel Dominik, Alexander Tornede, Felix Mohr, and Eyke Hüllermeier. “AutoML for Multi-Label Classification: Overview and Empirical Evaluation.” <i>IEEE Transactions on Pattern Analysis and Machine Intelligence</i>, 2021, 1–1. <a href=\"https://doi.org/10.1109/tpami.2021.3051276\">https://doi.org/10.1109/tpami.2021.3051276</a>."},"year":"2021","publication_identifier":{"issn":["0162-8828","2160-9292","1939-3539"]},"publication_status":"published","doi":"10.1109/tpami.2021.3051276","title":"AutoML for Multi-Label Classification: Overview and Empirical Evaluation","author":[{"first_name":"Marcel Dominik","orcid":" https://orcid.org/0000-0001-9782-6818","last_name":"Wever","id":"33176","full_name":"Wever, Marcel Dominik"},{"last_name":"Tornede","id":"38209","full_name":"Tornede, Alexander","first_name":"Alexander"},{"first_name":"Felix","last_name":"Mohr","full_name":"Mohr, Felix"},{"first_name":"Eyke","full_name":"Hüllermeier, Eyke","id":"48129","last_name":"Hüllermeier"}],"date_created":"2021-01-16T14:48:13Z","date_updated":"2022-01-06T06:54:42Z","status":"public","abstract":[{"text":"Automated machine learning (AutoML) supports the algorithmic construction and data-specific customization of machine learning pipelines, including the selection, combination, and parametrization of machine learning algorithms as main constituents. Generally speaking, AutoML approaches comprise two major components: a search space model and an optimizer for traversing the space. Recent approaches have shown impressive results in the realm of supervised learning, most notably (single-label) classification (SLC). Moreover, first attempts at extending these approaches towards multi-label classification (MLC) have been made. While the space of candidate pipelines is already huge in SLC, the complexity of the search space is raised to an even higher power in MLC. One may wonder, therefore, whether and to what extent optimizers established for SLC can scale to this increased complexity, and how they compare to each other. This paper makes the following contributions: First, we survey existing approaches to AutoML for MLC. Second, we augment these approaches with optimizers not previously tried for MLC. Third, we propose a benchmarking framework that supports a fair and systematic comparison. Fourth, we conduct an extensive experimental study, evaluating the methods on a suite of MLC problems. We find a grammar-based best-first search to compare favorably to other optimizers.","lang":"eng"}],"publication":"IEEE Transactions on Pattern Analysis and Machine Intelligence","type":"journal_article","language":[{"iso":"eng"}],"keyword":["Automated Machine Learning","Multi Label Classification","Hierarchical Planning","Bayesian Optimization"],"department":[{"_id":"34"},{"_id":"355"},{"_id":"26"}],"user_id":"5786","_id":"21004","project":[{"name":"SFB 901","_id":"1"},{"_id":"3","name":"SFB 901 - Project Area B"},{"_id":"10","name":"SFB 901 - Subproject B2"},{"_id":"52","name":"Computing Resources Provided by the Paderborn Center for Parallel Computing"}]},{"oa":"1","date_updated":"2022-01-06T06:54:32Z","author":[{"last_name":"Schneider","orcid":"0000-0001-8210-4011","full_name":"Schneider, Stefan Balthasar","id":"35343","first_name":"Stefan Balthasar"},{"first_name":"Mirko","last_name":"Jürgens","full_name":"Jürgens, Mirko"},{"first_name":"Holger","last_name":"Karl","full_name":"Karl, Holger","id":"126"}],"conference":{"name":"IFIP/IEEE International Symposium on Integrated Network Management (IM)","location":"Bordeaux, France"},"has_accepted_license":"1","citation":{"mla":"Schneider, Stefan Balthasar, et al. “Divide and Conquer: Hierarchical Network and Service Coordination.” <i>IFIP/IEEE International Symposium on Integrated Network Management (IM)</i>, IFIP/IEEE, 2021.","bibtex":"@inproceedings{Schneider_Jürgens_Karl_2021, title={Divide and Conquer: Hierarchical Network and Service Coordination}, booktitle={IFIP/IEEE International Symposium on Integrated Network Management (IM)}, publisher={IFIP/IEEE}, author={Schneider, Stefan Balthasar and Jürgens, Mirko and Karl, Holger}, year={2021} }","short":"S.B. Schneider, M. Jürgens, H. Karl, in: IFIP/IEEE International Symposium on Integrated Network Management (IM), IFIP/IEEE, 2021.","apa":"Schneider, S. B., Jürgens, M., &#38; Karl, H. (2021). Divide and Conquer: Hierarchical Network and Service Coordination. In <i>IFIP/IEEE International Symposium on Integrated Network Management (IM)</i>. Bordeaux, France: IFIP/IEEE.","ieee":"S. B. Schneider, M. Jürgens, and H. Karl, “Divide and Conquer: Hierarchical Network and Service Coordination,” in <i>IFIP/IEEE International Symposium on Integrated Network Management (IM)</i>, Bordeaux, France, 2021.","chicago":"Schneider, Stefan Balthasar, Mirko Jürgens, and Holger Karl. “Divide and Conquer: Hierarchical Network and Service Coordination.” In <i>IFIP/IEEE International Symposium on Integrated Network Management (IM)</i>. IFIP/IEEE, 2021.","ama":"Schneider SB, Jürgens M, Karl H. Divide and Conquer: Hierarchical Network and Service Coordination. In: <i>IFIP/IEEE International Symposium on Integrated Network Management (IM)</i>. IFIP/IEEE; 2021."},"project":[{"name":"SFB 901","_id":"1"},{"name":"SFB 901 - Project Area C","_id":"4"},{"_id":"16","name":"SFB 901 - Subproject C4"}],"_id":"20693","user_id":"35343","department":[{"_id":"75"}],"file_date_updated":"2020-12-11T08:37:37Z","type":"conference","status":"public","publisher":"IFIP/IEEE","date_created":"2020-12-11T08:39:47Z","title":"Divide and Conquer: Hierarchical Network and Service Coordination","quality_controlled":"1","year":"2021","ddc":["006"],"keyword":["network management","service management","coordination","hierarchical","scalability","nfv"],"language":[{"iso":"eng"}],"publication":"IFIP/IEEE International Symposium on Integrated Network Management (IM)","abstract":[{"text":"In practical, large-scale networks, services are requested\r\nby users across the globe, e.g., for video streaming.\r\nServices consist of multiple interconnected components such as\r\nmicroservices in a service mesh. Coordinating these services\r\nrequires scaling them according to continuously changing user\r\ndemand, deploying instances at the edge close to their users,\r\nand routing traffic efficiently between users and connected instances.\r\nNetwork and service coordination is commonly addressed\r\nthrough centralized approaches, where a single coordinator\r\nknows everything and coordinates the entire network globally.\r\nWhile such centralized approaches can reach global optima, they\r\ndo not scale to large, realistic networks. In contrast, distributed\r\napproaches scale well, but sacrifice solution quality due to their\r\nlimited scope of knowledge and coordination decisions.\r\n\r\nTo this end, we propose a hierarchical coordination approach\r\nthat combines the good solution quality of centralized approaches\r\nwith the scalability of distributed approaches. In doing so, we divide\r\nthe network into multiple hierarchical domains and optimize\r\ncoordination in a top-down manner. We compare our hierarchical\r\nwith a centralized approach in an extensive evaluation on a real-world\r\nnetwork topology. Our results indicate that hierarchical\r\ncoordination can find close-to-optimal solutions in a fraction of\r\nthe runtime of centralized approaches.","lang":"eng"}],"file":[{"content_type":"application/pdf","relation":"main_file","creator":"stschn","date_created":"2020-12-11T08:37:37Z","date_updated":"2020-12-11T08:37:37Z","access_level":"open_access","file_id":"20694","file_name":"preprint_with_header.pdf","title":"Divide and Conquer: Hierarchical Network and Service Coordination","file_size":7979772}]},{"publication_identifier":{"eissn":["1573-0565"],"issn":["0885-6125"]},"has_accepted_license":"1","publication_status":"epub_ahead","page":"1495-1515","citation":{"ieee":"F. Mohr, M. D. Wever, and E. Hüllermeier, “ML-Plan: Automated Machine Learning via Hierarchical Planning,” <i>Machine Learning</i>, pp. 1495–1515, 2018, doi: <a href=\"https://doi.org/10.1007/s10994-018-5735-z\">10.1007/s10994-018-5735-z</a>.","chicago":"Mohr, Felix, Marcel Dominik Wever, and Eyke Hüllermeier. “ML-Plan: Automated Machine Learning via Hierarchical Planning.” <i>Machine Learning</i>, 2018, 1495–1515. <a href=\"https://doi.org/10.1007/s10994-018-5735-z\">https://doi.org/10.1007/s10994-018-5735-z</a>.","ama":"Mohr F, Wever MD, Hüllermeier E. ML-Plan: Automated Machine Learning via Hierarchical Planning. <i>Machine Learning</i>. Published online 2018:1495-1515. doi:<a href=\"https://doi.org/10.1007/s10994-018-5735-z\">10.1007/s10994-018-5735-z</a>","apa":"Mohr, F., Wever, M. D., &#38; Hüllermeier, E. (2018). ML-Plan: Automated Machine Learning via Hierarchical Planning. <i>Machine Learning</i>, 1495–1515. <a href=\"https://doi.org/10.1007/s10994-018-5735-z\">https://doi.org/10.1007/s10994-018-5735-z</a>","short":"F. Mohr, M.D. Wever, E. Hüllermeier, Machine Learning (2018) 1495–1515.","bibtex":"@article{Mohr_Wever_Hüllermeier_2018, title={ML-Plan: Automated Machine Learning via Hierarchical Planning}, DOI={<a href=\"https://doi.org/10.1007/s10994-018-5735-z\">10.1007/s10994-018-5735-z</a>}, journal={Machine Learning}, publisher={Springer}, author={Mohr, Felix and Wever, Marcel Dominik and Hüllermeier, Eyke}, year={2018}, pages={1495–1515} }","mla":"Mohr, Felix, et al. “ML-Plan: Automated Machine Learning via Hierarchical Planning.” <i>Machine Learning</i>, Springer, 2018, pp. 1495–515, doi:<a href=\"https://doi.org/10.1007/s10994-018-5735-z\">10.1007/s10994-018-5735-z</a>."},"date_updated":"2022-01-06T06:59:21Z","oa":"1","author":[{"first_name":"Felix","last_name":"Mohr","full_name":"Mohr, Felix"},{"id":"33176","full_name":"Wever, Marcel Dominik","orcid":" https://orcid.org/0000-0001-9782-6818","last_name":"Wever","first_name":"Marcel Dominik"},{"first_name":"Eyke","last_name":"Hüllermeier","full_name":"Hüllermeier, Eyke","id":"48129"}],"conference":{"start_date":"2018-09-10","name":"European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases","location":"Dublin, Ireland","end_date":"2018-09-14"},"doi":"10.1007/s10994-018-5735-z","main_file_link":[{"url":"https://rdcu.be/3Nc2","open_access":"1"}],"type":"journal_article","status":"public","_id":"3510","project":[{"name":"SFB 901","_id":"1"},{"name":"SFB 901 - Project Area B","_id":"3"},{"name":"SFB 901 - Subproject B2","_id":"10"},{"name":"Computing Resources Provided by the Paderborn Center for Parallel Computing","_id":"52"}],"department":[{"_id":"355"},{"_id":"34"},{"_id":"7"},{"_id":"26"}],"user_id":"5786","article_type":"original","file_date_updated":"2018-11-02T15:32:16Z","year":"2018","publisher":"Springer","date_created":"2018-07-08T14:06:14Z","title":"ML-Plan: Automated Machine Learning via Hierarchical Planning","publication":"Machine Learning","abstract":[{"lang":"eng","text":"Automated machine learning (AutoML) seeks to automatically select, compose, and parametrize machine learning algorithms, so as to achieve optimal performance on a given task (dataset). Although current approaches to AutoML have already produced impressive results, the field is still far from mature, and new techniques are still being developed. In this paper, we present ML-Plan, a new approach to AutoML based on hierarchical planning. To highlight the potential of this approach, we compare ML-Plan to the state-of-the-art frameworks Auto-WEKA, auto-sklearn, and TPOT. In an extensive series of experiments, we show that ML-Plan is highly competitive and often outperforms existing approaches."}],"file":[{"success":1,"relation":"main_file","content_type":"application/pdf","file_size":1070937,"file_name":"ML-PlanAutomatedMachineLearnin.pdf","access_level":"closed","file_id":"5306","date_updated":"2018-11-02T15:32:16Z","creator":"ups","date_created":"2018-11-02T15:32:16Z"}],"keyword":["AutoML","Hierarchical Planning","HTN planning","ML-Plan"],"ddc":["000"],"language":[{"iso":"eng"}]},{"author":[{"id":"33176","full_name":"Wever, Marcel Dominik","last_name":"Wever","orcid":" https://orcid.org/0000-0001-9782-6818","first_name":"Marcel Dominik"},{"full_name":"Mohr, Felix","last_name":"Mohr","first_name":"Felix"},{"full_name":"Hüllermeier, Eyke","id":"48129","last_name":"Hüllermeier","first_name":"Eyke"}],"oa":"1","date_updated":"2022-01-06T06:59:46Z","conference":{"location":"Stockholm, Sweden","end_date":"2018-07-15","start_date":"2018-07-10","name":"ICML 2018 AutoML Workshop"},"main_file_link":[{"url":"https://docs.google.com/viewer?a=v&pid=sites&srcid=ZGVmYXVsdGRvbWFpbnxhdXRvbWwyMDE4aWNtbHxneDo3M2Q3MjUzYjViNDRhZTAx"}],"has_accepted_license":"1","citation":{"bibtex":"@inproceedings{Wever_Mohr_Hüllermeier_2018, title={ML-Plan for Unlimited-Length Machine Learning Pipelines}, booktitle={ICML 2018 AutoML Workshop}, author={Wever, Marcel Dominik and Mohr, Felix and Hüllermeier, Eyke}, year={2018} }","mla":"Wever, Marcel Dominik, et al. “ML-Plan for Unlimited-Length Machine Learning Pipelines.” <i>ICML 2018 AutoML Workshop</i>, 2018.","short":"M.D. Wever, F. Mohr, E. Hüllermeier, in: ICML 2018 AutoML Workshop, 2018.","apa":"Wever, M. D., Mohr, F., &#38; Hüllermeier, E. (2018). ML-Plan for Unlimited-Length Machine Learning Pipelines. In <i>ICML 2018 AutoML Workshop</i>. Stockholm, Sweden.","ama":"Wever MD, Mohr F, Hüllermeier E. ML-Plan for Unlimited-Length Machine Learning Pipelines. In: <i>ICML 2018 AutoML Workshop</i>. ; 2018.","chicago":"Wever, Marcel Dominik, Felix Mohr, and Eyke Hüllermeier. “ML-Plan for Unlimited-Length Machine Learning Pipelines.” In <i>ICML 2018 AutoML Workshop</i>, 2018.","ieee":"M. D. Wever, F. Mohr, and E. Hüllermeier, “ML-Plan for Unlimited-Length Machine Learning Pipelines,” in <i>ICML 2018 AutoML Workshop</i>, Stockholm, Sweden, 2018."},"department":[{"_id":"355"}],"user_id":"49109","_id":"3852","project":[{"_id":"1","name":"SFB 901"},{"name":"SFB 901 - Project Area B","_id":"3"},{"name":"SFB 901 - Subproject B2","_id":"10"}],"file_date_updated":"2018-08-09T06:14:43Z","type":"conference","status":"public","urn":"38527","date_created":"2018-08-09T06:14:54Z","title":"ML-Plan for Unlimited-Length Machine Learning Pipelines","quality_controlled":"1","year":"2018","language":[{"iso":"eng"}],"keyword":["automated machine learning","complex pipelines","hierarchical planning"],"ddc":["006"],"publication":"ICML 2018 AutoML Workshop","file":[{"content_type":"application/pdf","relation":"main_file","date_updated":"2018-08-09T06:14:43Z","creator":"wever","date_created":"2018-08-09T06:14:43Z","file_size":297811,"access_level":"open_access","file_id":"3853","file_name":"38.pdf"}],"abstract":[{"lang":"eng","text":"In automated machine learning (AutoML), the process of engineering machine learning applications with respect to a specific problem is (partially) automated.\r\nVarious AutoML tools have already been introduced to provide out-of-the-box machine learning functionality.\r\nMore specifically, by selecting machine learning algorithms and optimizing their hyperparameters, these tools produce a machine learning pipeline tailored to the problem at hand.\r\nExcept for TPOT, all of these tools restrict the maximum number of processing steps of such a pipeline.\r\nHowever, as TPOT follows an evolutionary approach, it suffers from performance issues when dealing with larger datasets.\r\nIn this paper, we present an alternative approach leveraging a hierarchical planning to configure machine learning pipelines that are unlimited in length.\r\nWe evaluate our approach and find its performance to be competitive with other AutoML tools, including TPOT."}]},{"place":"Kyoto, Japan","citation":{"ama":"Wever MD, Mohr F, Hüllermeier E. Ensembles of Evolved Nested Dichotomies for Classification. In: <i>Proceedings of the Genetic and Evolutionary Computation Conference, GECCO 2018, Kyoto, Japan, July 15-19, 2018</i>. Kyoto, Japan: ACM; 2018. doi:<a href=\"https://doi.org/10.1145/3205455.3205562\">10.1145/3205455.3205562</a>","chicago":"Wever, Marcel Dominik, Felix Mohr, and Eyke Hüllermeier. “Ensembles of Evolved Nested Dichotomies for Classification.” In <i>Proceedings of the Genetic and Evolutionary Computation Conference, GECCO 2018, Kyoto, Japan, July 15-19, 2018</i>. Kyoto, Japan: ACM, 2018. <a href=\"https://doi.org/10.1145/3205455.3205562\">https://doi.org/10.1145/3205455.3205562</a>.","ieee":"M. D. Wever, F. Mohr, and E. Hüllermeier, “Ensembles of Evolved Nested Dichotomies for Classification,” in <i>Proceedings of the Genetic and Evolutionary Computation Conference, GECCO 2018, Kyoto, Japan, July 15-19, 2018</i>, Kyoto, Japan, 2018.","mla":"Wever, Marcel Dominik, et al. “Ensembles of Evolved Nested Dichotomies for Classification.” <i>Proceedings of the Genetic and Evolutionary Computation Conference, GECCO 2018, Kyoto, Japan, July 15-19, 2018</i>, ACM, 2018, doi:<a href=\"https://doi.org/10.1145/3205455.3205562\">10.1145/3205455.3205562</a>.","short":"M.D. Wever, F. Mohr, E. Hüllermeier, in: Proceedings of the Genetic and Evolutionary Computation Conference, GECCO 2018, Kyoto, Japan, July 15-19, 2018, ACM, Kyoto, Japan, 2018.","bibtex":"@inproceedings{Wever_Mohr_Hüllermeier_2018, place={Kyoto, Japan}, title={Ensembles of Evolved Nested Dichotomies for Classification}, DOI={<a href=\"https://doi.org/10.1145/3205455.3205562\">10.1145/3205455.3205562</a>}, booktitle={Proceedings of the Genetic and Evolutionary Computation Conference, GECCO 2018, Kyoto, Japan, July 15-19, 2018}, publisher={ACM}, author={Wever, Marcel Dominik and Mohr, Felix and Hüllermeier, Eyke}, year={2018} }","apa":"Wever, M. D., Mohr, F., &#38; Hüllermeier, E. (2018). Ensembles of Evolved Nested Dichotomies for Classification. In <i>Proceedings of the Genetic and Evolutionary Computation Conference, GECCO 2018, Kyoto, Japan, July 15-19, 2018</i>. Kyoto, Japan: ACM. <a href=\"https://doi.org/10.1145/3205455.3205562\">https://doi.org/10.1145/3205455.3205562</a>"},"publication_status":"published","has_accepted_license":"1","main_file_link":[{"url":"https://dl.acm.org/citation.cfm?doid=3205455.3205562","open_access":"1"}],"doi":"10.1145/3205455.3205562","conference":{"end_date":"2018-07-19","location":"Kyoto, Japan","name":"GECCO 2018","start_date":"2018-07-15"},"date_updated":"2022-01-06T06:54:45Z","oa":"1","author":[{"first_name":"Marcel Dominik","orcid":" https://orcid.org/0000-0001-9782-6818","last_name":"Wever","id":"33176","full_name":"Wever, Marcel Dominik"},{"first_name":"Felix","last_name":"Mohr","full_name":"Mohr, Felix"},{"first_name":"Eyke","id":"48129","full_name":"Hüllermeier, Eyke","last_name":"Hüllermeier"}],"status":"public","type":"conference","file_date_updated":"2018-11-02T14:33:54Z","project":[{"name":"SFB 901","_id":"1"},{"_id":"3","name":"SFB 901 - Project Area B"},{"_id":"10","name":"SFB 901 - Subproject B2"},{"name":"Computing Resources Provided by the Paderborn Center for Parallel Computing","_id":"52"}],"_id":"2109","user_id":"33176","department":[{"_id":"355"}],"year":"2018","title":"Ensembles of Evolved Nested Dichotomies for Classification","publisher":"ACM","date_created":"2018-03-31T13:51:23Z","abstract":[{"text":"In multinomial classification, reduction techniques are commonly used to decompose the original learning problem into several simpler problems. For example, by recursively bisecting the original set of classes, so-called nested dichotomies define a set of binary classification problems that are organized in the structure of a binary tree. In contrast to the existing one-shot heuristics for constructing nested dichotomies and motivated by recent work on algorithm configuration, we propose a genetic algorithm for optimizing the structure of such dichotomies. A key component of this approach is the proposed genetic representation that facilitates the application of standard genetic operators, while still supporting the exchange of partial solutions under recombination. We evaluate the approach in an extensive experimental study, showing that it yields classifiers with superior generalization performance.","lang":"eng"}],"file":[{"content_type":"application/pdf","relation":"main_file","success":1,"creator":"ups","date_created":"2018-11-02T14:33:54Z","date_updated":"2018-11-02T14:33:54Z","file_name":"p561-wever.pdf","file_id":"5275","access_level":"closed","file_size":875404}],"publication":"Proceedings of the Genetic and Evolutionary Computation Conference, GECCO 2018, Kyoto, Japan, July 15-19, 2018","ddc":["000"],"keyword":["Classification","Hierarchical Decomposition","Indirect Encoding"],"language":[{"iso":"eng"}]},{"user_id":"460","department":[{"_id":"54"}],"_id":"11806","language":[{"iso":"eng"}],"keyword":["acoustic sensing tasks","array geometry","calibration","coherence analysis","hierarchical procedure","local shape calibration","microphone array networks","microphone arrays","network calibration method","sensor arrays","SRP-PHAT","unsupervised shape calibration"],"type":"conference","publication":"IEEE/SP 15th Workshop on Statistical Signal Processing (SSP 2009)","status":"public","abstract":[{"text":"Microphone arrays represent the basis for many challenging acoustic sensing tasks. The accuracy of techniques like beamforming directly depends on a precise knowledge of the relative positions of the sensors used. Unfortunately, for certain use cases manually measuring the geometry of an array is not feasible due to practical constraints. In this paper we present an approach to unsupervised shape calibration of microphone array networks. We developed a hierarchical procedure that first performs local shape calibration based on coherence analysis and then employs SRP-PHAT in a network calibration method. Practical experiments demonstrate the effectiveness of our approach especially for highly reverberant acoustic environments.","lang":"eng"}],"author":[{"first_name":"Marius","last_name":"Hennecke","full_name":"Hennecke, Marius"},{"full_name":"Ploetz, Thomas","last_name":"Ploetz","first_name":"Thomas"},{"first_name":"Gernot A.","last_name":"Fink","full_name":"Fink, Gernot A."},{"full_name":"Schmalenstroeer, Joerg","id":"460","last_name":"Schmalenstroeer","first_name":"Joerg"},{"first_name":"Reinhold","full_name":"Haeb-Umbach, Reinhold","id":"242","last_name":"Haeb-Umbach"}],"date_created":"2019-07-12T05:28:37Z","date_updated":"2023-10-26T08:09:22Z","oa":"1","main_file_link":[{"url":"https://groups.uni-paderborn.de/nt/pubs/2009/HePlFiScHa09.pdf","open_access":"1"}],"doi":"10.1109/SSP.2009.5278589","title":"A hierarchical approach to unsupervised shape calibration of microphone array networks","quality_controlled":"1","citation":{"ama":"Hennecke M, Ploetz T, Fink GA, Schmalenstroeer J, Haeb-Umbach R. A hierarchical approach to unsupervised shape calibration of microphone array networks. In: <i>IEEE/SP 15th Workshop on Statistical Signal Processing (SSP 2009)</i>. ; 2009:257-260. doi:<a href=\"https://doi.org/10.1109/SSP.2009.5278589\">10.1109/SSP.2009.5278589</a>","ieee":"M. Hennecke, T. Ploetz, G. A. Fink, J. Schmalenstroeer, and R. Haeb-Umbach, “A hierarchical approach to unsupervised shape calibration of microphone array networks,” in <i>IEEE/SP 15th Workshop on Statistical Signal Processing (SSP 2009)</i>, 2009, pp. 257–260, doi: <a href=\"https://doi.org/10.1109/SSP.2009.5278589\">10.1109/SSP.2009.5278589</a>.","chicago":"Hennecke, Marius, Thomas Ploetz, Gernot A. Fink, Joerg Schmalenstroeer, and Reinhold Haeb-Umbach. “A Hierarchical Approach to Unsupervised Shape Calibration of Microphone Array Networks.” In <i>IEEE/SP 15th Workshop on Statistical Signal Processing (SSP 2009)</i>, 257–60, 2009. <a href=\"https://doi.org/10.1109/SSP.2009.5278589\">https://doi.org/10.1109/SSP.2009.5278589</a>.","apa":"Hennecke, M., Ploetz, T., Fink, G. A., Schmalenstroeer, J., &#38; Haeb-Umbach, R. (2009). A hierarchical approach to unsupervised shape calibration of microphone array networks. <i>IEEE/SP 15th Workshop on Statistical Signal Processing (SSP 2009)</i>, 257–260. <a href=\"https://doi.org/10.1109/SSP.2009.5278589\">https://doi.org/10.1109/SSP.2009.5278589</a>","short":"M. Hennecke, T. Ploetz, G.A. Fink, J. Schmalenstroeer, R. Haeb-Umbach, in: IEEE/SP 15th Workshop on Statistical Signal Processing (SSP 2009), 2009, pp. 257–260.","bibtex":"@inproceedings{Hennecke_Ploetz_Fink_Schmalenstroeer_Haeb-Umbach_2009, title={A hierarchical approach to unsupervised shape calibration of microphone array networks}, DOI={<a href=\"https://doi.org/10.1109/SSP.2009.5278589\">10.1109/SSP.2009.5278589</a>}, booktitle={IEEE/SP 15th Workshop on Statistical Signal Processing (SSP 2009)}, author={Hennecke, Marius and Ploetz, Thomas and Fink, Gernot A. and Schmalenstroeer, Joerg and Haeb-Umbach, Reinhold}, year={2009}, pages={257–260} }","mla":"Hennecke, Marius, et al. “A Hierarchical Approach to Unsupervised Shape Calibration of Microphone Array Networks.” <i>IEEE/SP 15th Workshop on Statistical Signal Processing (SSP 2009)</i>, 2009, pp. 257–60, doi:<a href=\"https://doi.org/10.1109/SSP.2009.5278589\">10.1109/SSP.2009.5278589</a>."},"page":"257-260","year":"2009"}]