---
_id: '21004'
abstract:
- lang: eng
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.'
author:
- first_name: Marcel Dominik
full_name: Wever, Marcel Dominik
id: '33176'
last_name: Wever
orcid: ' https://orcid.org/0000-0001-9782-6818'
- first_name: Alexander
full_name: Tornede, Alexander
id: '38209'
last_name: Tornede
- first_name: Felix
full_name: Mohr, Felix
last_name: Mohr
- first_name: Eyke
full_name: Hüllermeier, Eyke
id: '48129'
last_name: Hüllermeier
citation:
ama: 'Wever MD, Tornede A, Mohr F, Hüllermeier E. AutoML for Multi-Label Classification:
Overview and Empirical Evaluation. IEEE Transactions on Pattern Analysis and
Machine Intelligence. Published online 2021:1-1. doi:10.1109/tpami.2021.3051276'
apa: 'Wever, M. D., Tornede, A., Mohr, F., & Hüllermeier, E. (2021). AutoML
for Multi-Label Classification: Overview and Empirical Evaluation. IEEE Transactions
on Pattern Analysis and Machine Intelligence, 1–1. https://doi.org/10.1109/tpami.2021.3051276'
bibtex: '@article{Wever_Tornede_Mohr_Hüllermeier_2021, title={AutoML for Multi-Label
Classification: Overview and Empirical Evaluation}, DOI={10.1109/tpami.2021.3051276},
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} }'
chicago: 'Wever, Marcel Dominik, Alexander Tornede, Felix Mohr, and Eyke Hüllermeier.
“AutoML for Multi-Label Classification: Overview and Empirical Evaluation.” IEEE
Transactions on Pattern Analysis and Machine Intelligence, 2021, 1–1. https://doi.org/10.1109/tpami.2021.3051276.'
ieee: 'M. D. Wever, A. Tornede, F. Mohr, and E. Hüllermeier, “AutoML for Multi-Label
Classification: Overview and Empirical Evaluation,” IEEE Transactions on Pattern
Analysis and Machine Intelligence, pp. 1–1, 2021, doi: 10.1109/tpami.2021.3051276.'
mla: 'Wever, Marcel Dominik, et al. “AutoML for Multi-Label Classification: Overview
and Empirical Evaluation.” IEEE Transactions on Pattern Analysis and Machine
Intelligence, 2021, pp. 1–1, doi:10.1109/tpami.2021.3051276.'
short: M.D. Wever, A. Tornede, F. Mohr, E. Hüllermeier, IEEE Transactions on Pattern
Analysis and Machine Intelligence (2021) 1–1.
date_created: 2021-01-16T14:48:13Z
date_updated: 2022-01-06T06:54:42Z
department:
- _id: '34'
- _id: '355'
- _id: '26'
doi: 10.1109/tpami.2021.3051276
keyword:
- Automated Machine Learning
- Multi Label Classification
- Hierarchical Planning
- Bayesian Optimization
language:
- iso: eng
page: 1-1
project:
- _id: '1'
name: SFB 901
- _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
publication: IEEE Transactions on Pattern Analysis and Machine Intelligence
publication_identifier:
issn:
- 0162-8828
- 2160-9292
- 1939-3539
publication_status: published
status: public
title: 'AutoML for Multi-Label Classification: Overview and Empirical Evaluation'
type: journal_article
user_id: '5786'
year: '2021'
...
---
_id: '20693'
abstract:
- lang: eng
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."
author:
- first_name: Stefan Balthasar
full_name: Schneider, Stefan Balthasar
id: '35343'
last_name: Schneider
orcid: 0000-0001-8210-4011
- first_name: Mirko
full_name: Jürgens, Mirko
last_name: Jürgens
- first_name: Holger
full_name: Karl, Holger
id: '126'
last_name: Karl
citation:
ama: 'Schneider SB, Jürgens M, Karl H. Divide and Conquer: Hierarchical Network
and Service Coordination. In: IFIP/IEEE International Symposium on Integrated
Network Management (IM). IFIP/IEEE; 2021.'
apa: 'Schneider, S. B., Jürgens, M., & Karl, H. (2021). Divide and Conquer:
Hierarchical Network and Service Coordination. In IFIP/IEEE International Symposium
on Integrated Network Management (IM). Bordeaux, France: IFIP/IEEE.'
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} }'
chicago: 'Schneider, Stefan Balthasar, Mirko Jürgens, and Holger Karl. “Divide and
Conquer: Hierarchical Network and Service Coordination.” In IFIP/IEEE International
Symposium on Integrated Network Management (IM). IFIP/IEEE, 2021.'
ieee: 'S. B. Schneider, M. Jürgens, and H. Karl, “Divide and Conquer: Hierarchical
Network and Service Coordination,” in IFIP/IEEE International Symposium on
Integrated Network Management (IM), Bordeaux, France, 2021.'
mla: 'Schneider, Stefan Balthasar, et al. “Divide and Conquer: Hierarchical Network
and Service Coordination.” IFIP/IEEE International Symposium on Integrated
Network Management (IM), IFIP/IEEE, 2021.'
short: 'S.B. Schneider, M. Jürgens, H. Karl, in: IFIP/IEEE International Symposium
on Integrated Network Management (IM), IFIP/IEEE, 2021.'
conference:
location: Bordeaux, France
name: IFIP/IEEE International Symposium on Integrated Network Management (IM)
date_created: 2020-12-11T08:39:47Z
date_updated: 2022-01-06T06:54:32Z
ddc:
- '006'
department:
- _id: '75'
file:
- access_level: open_access
content_type: application/pdf
creator: stschn
date_created: 2020-12-11T08:37:37Z
date_updated: 2020-12-11T08:37:37Z
file_id: '20694'
file_name: preprint_with_header.pdf
file_size: 7979772
relation: main_file
title: 'Divide and Conquer: Hierarchical Network and Service Coordination'
file_date_updated: 2020-12-11T08:37:37Z
has_accepted_license: '1'
keyword:
- network management
- service management
- coordination
- hierarchical
- scalability
- nfv
language:
- iso: eng
oa: '1'
project:
- _id: '1'
name: SFB 901
- _id: '4'
name: SFB 901 - Project Area C
- _id: '16'
name: SFB 901 - Subproject C4
publication: IFIP/IEEE International Symposium on Integrated Network Management (IM)
publisher: IFIP/IEEE
quality_controlled: '1'
status: public
title: 'Divide and Conquer: Hierarchical Network and Service Coordination'
type: conference
user_id: '35343'
year: '2021'
...
---
_id: '3510'
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.
article_type: original
author:
- first_name: Felix
full_name: Mohr, Felix
last_name: Mohr
- first_name: Marcel Dominik
full_name: Wever, Marcel Dominik
id: '33176'
last_name: Wever
orcid: ' https://orcid.org/0000-0001-9782-6818'
- first_name: Eyke
full_name: Hüllermeier, Eyke
id: '48129'
last_name: Hüllermeier
citation:
ama: 'Mohr F, Wever MD, Hüllermeier E. ML-Plan: Automated Machine Learning via Hierarchical
Planning. Machine Learning. Published online 2018:1495-1515. doi:10.1007/s10994-018-5735-z'
apa: 'Mohr, F., Wever, M. D., & Hüllermeier, E. (2018). ML-Plan: Automated Machine
Learning via Hierarchical Planning. Machine Learning, 1495–1515. https://doi.org/10.1007/s10994-018-5735-z'
bibtex: '@article{Mohr_Wever_Hüllermeier_2018, title={ML-Plan: Automated Machine
Learning via Hierarchical Planning}, DOI={10.1007/s10994-018-5735-z},
journal={Machine Learning}, publisher={Springer}, author={Mohr, Felix and Wever,
Marcel Dominik and Hüllermeier, Eyke}, year={2018}, pages={1495–1515} }'
chicago: 'Mohr, Felix, Marcel Dominik Wever, and Eyke Hüllermeier. “ML-Plan: Automated
Machine Learning via Hierarchical Planning.” Machine Learning, 2018, 1495–1515.
https://doi.org/10.1007/s10994-018-5735-z.'
ieee: 'F. Mohr, M. D. Wever, and E. Hüllermeier, “ML-Plan: Automated Machine Learning
via Hierarchical Planning,” Machine Learning, pp. 1495–1515, 2018, doi:
10.1007/s10994-018-5735-z.'
mla: 'Mohr, Felix, et al. “ML-Plan: Automated Machine Learning via Hierarchical
Planning.” Machine Learning, Springer, 2018, pp. 1495–515, doi:10.1007/s10994-018-5735-z.'
short: F. Mohr, M.D. Wever, E. Hüllermeier, Machine Learning (2018) 1495–1515.
conference:
end_date: 2018-09-14
location: Dublin, Ireland
name: European Conference on Machine Learning and Principles and Practice of Knowledge
Discovery in Databases
start_date: 2018-09-10
date_created: 2018-07-08T14:06:14Z
date_updated: 2022-01-06T06:59:21Z
ddc:
- '000'
department:
- _id: '355'
- _id: '34'
- _id: '7'
- _id: '26'
doi: 10.1007/s10994-018-5735-z
file:
- access_level: closed
content_type: application/pdf
creator: ups
date_created: 2018-11-02T15:32:16Z
date_updated: 2018-11-02T15:32:16Z
file_id: '5306'
file_name: ML-PlanAutomatedMachineLearnin.pdf
file_size: 1070937
relation: main_file
success: 1
file_date_updated: 2018-11-02T15:32:16Z
has_accepted_license: '1'
keyword:
- AutoML
- Hierarchical Planning
- HTN planning
- ML-Plan
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://rdcu.be/3Nc2
oa: '1'
page: 1495-1515
project:
- _id: '1'
name: SFB 901
- _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
publication: Machine Learning
publication_identifier:
eissn:
- 1573-0565
issn:
- 0885-6125
publication_status: epub_ahead
publisher: Springer
status: public
title: 'ML-Plan: Automated Machine Learning via Hierarchical Planning'
type: journal_article
user_id: '5786'
year: '2018'
...
---
_id: '3852'
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."
author:
- first_name: Marcel Dominik
full_name: Wever, Marcel Dominik
id: '33176'
last_name: Wever
orcid: ' https://orcid.org/0000-0001-9782-6818'
- first_name: Felix
full_name: Mohr, Felix
last_name: Mohr
- first_name: Eyke
full_name: Hüllermeier, Eyke
id: '48129'
last_name: Hüllermeier
citation:
ama: 'Wever MD, Mohr F, Hüllermeier E. ML-Plan for Unlimited-Length Machine Learning
Pipelines. In: ICML 2018 AutoML Workshop. ; 2018.'
apa: Wever, M. D., Mohr, F., & Hüllermeier, E. (2018). ML-Plan for Unlimited-Length
Machine Learning Pipelines. In ICML 2018 AutoML Workshop. Stockholm, Sweden.
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} }'
chicago: Wever, Marcel Dominik, Felix Mohr, and Eyke Hüllermeier. “ML-Plan for Unlimited-Length
Machine Learning Pipelines.” In ICML 2018 AutoML Workshop, 2018.
ieee: M. D. Wever, F. Mohr, and E. Hüllermeier, “ML-Plan for Unlimited-Length Machine
Learning Pipelines,” in ICML 2018 AutoML Workshop, Stockholm, Sweden, 2018.
mla: Wever, Marcel Dominik, et al. “ML-Plan for Unlimited-Length Machine Learning
Pipelines.” ICML 2018 AutoML Workshop, 2018.
short: 'M.D. Wever, F. Mohr, E. Hüllermeier, in: ICML 2018 AutoML Workshop, 2018.'
conference:
end_date: 2018-07-15
location: Stockholm, Sweden
name: ICML 2018 AutoML Workshop
start_date: 2018-07-10
date_created: 2018-08-09T06:14:54Z
date_updated: 2022-01-06T06:59:46Z
ddc:
- '006'
department:
- _id: '355'
file:
- access_level: open_access
content_type: application/pdf
creator: wever
date_created: 2018-08-09T06:14:43Z
date_updated: 2018-08-09T06:14:43Z
file_id: '3853'
file_name: 38.pdf
file_size: 297811
relation: main_file
file_date_updated: 2018-08-09T06:14:43Z
has_accepted_license: '1'
keyword:
- automated machine learning
- complex pipelines
- hierarchical planning
language:
- iso: eng
main_file_link:
- url: https://docs.google.com/viewer?a=v&pid=sites&srcid=ZGVmYXVsdGRvbWFpbnxhdXRvbWwyMDE4aWNtbHxneDo3M2Q3MjUzYjViNDRhZTAx
oa: '1'
project:
- _id: '1'
name: SFB 901
- _id: '3'
name: SFB 901 - Project Area B
- _id: '10'
name: SFB 901 - Subproject B2
publication: ICML 2018 AutoML Workshop
quality_controlled: '1'
status: public
title: ML-Plan for Unlimited-Length Machine Learning Pipelines
type: conference
urn: '38527'
user_id: '49109'
year: '2018'
...
---
_id: '2109'
abstract:
- lang: eng
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.
author:
- first_name: Marcel Dominik
full_name: Wever, Marcel Dominik
id: '33176'
last_name: Wever
orcid: ' https://orcid.org/0000-0001-9782-6818'
- first_name: Felix
full_name: Mohr, Felix
last_name: Mohr
- first_name: Eyke
full_name: Hüllermeier, Eyke
id: '48129'
last_name: Hüllermeier
citation:
ama: 'Wever MD, Mohr F, Hüllermeier E. Ensembles of Evolved Nested Dichotomies for
Classification. In: Proceedings of the Genetic and Evolutionary Computation
Conference, GECCO 2018, Kyoto, Japan, July 15-19, 2018. Kyoto, Japan: ACM;
2018. doi:10.1145/3205455.3205562'
apa: 'Wever, M. D., Mohr, F., & Hüllermeier, E. (2018). Ensembles of Evolved
Nested Dichotomies for Classification. In Proceedings of the Genetic and Evolutionary
Computation Conference, GECCO 2018, Kyoto, Japan, July 15-19, 2018. Kyoto,
Japan: ACM. https://doi.org/10.1145/3205455.3205562'
bibtex: '@inproceedings{Wever_Mohr_Hüllermeier_2018, place={Kyoto, Japan}, title={Ensembles
of Evolved Nested Dichotomies for Classification}, DOI={10.1145/3205455.3205562},
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} }'
chicago: 'Wever, Marcel Dominik, Felix Mohr, and Eyke Hüllermeier. “Ensembles of
Evolved Nested Dichotomies for Classification.” In Proceedings of the Genetic
and Evolutionary Computation Conference, GECCO 2018, Kyoto, Japan, July 15-19,
2018. Kyoto, Japan: ACM, 2018. https://doi.org/10.1145/3205455.3205562.'
ieee: M. D. Wever, F. Mohr, and E. Hüllermeier, “Ensembles of Evolved Nested Dichotomies
for Classification,” in Proceedings of the Genetic and Evolutionary Computation
Conference, GECCO 2018, Kyoto, Japan, July 15-19, 2018, Kyoto, Japan, 2018.
mla: Wever, Marcel Dominik, et al. “Ensembles of Evolved Nested Dichotomies for
Classification.” Proceedings of the Genetic and Evolutionary Computation Conference,
GECCO 2018, Kyoto, Japan, July 15-19, 2018, ACM, 2018, doi:10.1145/3205455.3205562.
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.'
conference:
end_date: 2018-07-19
location: Kyoto, Japan
name: GECCO 2018
start_date: 2018-07-15
date_created: 2018-03-31T13:51:23Z
date_updated: 2022-01-06T06:54:45Z
ddc:
- '000'
department:
- _id: '355'
doi: 10.1145/3205455.3205562
file:
- access_level: closed
content_type: application/pdf
creator: ups
date_created: 2018-11-02T14:33:54Z
date_updated: 2018-11-02T14:33:54Z
file_id: '5275'
file_name: p561-wever.pdf
file_size: 875404
relation: main_file
success: 1
file_date_updated: 2018-11-02T14:33:54Z
has_accepted_license: '1'
keyword:
- Classification
- Hierarchical Decomposition
- Indirect Encoding
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://dl.acm.org/citation.cfm?doid=3205455.3205562
oa: '1'
place: Kyoto, Japan
project:
- _id: '1'
name: SFB 901
- _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
publication: Proceedings of the Genetic and Evolutionary Computation Conference, GECCO
2018, Kyoto, Japan, July 15-19, 2018
publication_status: published
publisher: ACM
status: public
title: Ensembles of Evolved Nested Dichotomies for Classification
type: conference
user_id: '33176'
year: '2018'
...
---
_id: '11806'
abstract:
- lang: eng
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.
author:
- first_name: Marius
full_name: Hennecke, Marius
last_name: Hennecke
- first_name: Thomas
full_name: Ploetz, Thomas
last_name: Ploetz
- first_name: Gernot A.
full_name: Fink, Gernot A.
last_name: Fink
- first_name: Joerg
full_name: Schmalenstroeer, Joerg
id: '460'
last_name: Schmalenstroeer
- first_name: Reinhold
full_name: Haeb-Umbach, Reinhold
id: '242'
last_name: Haeb-Umbach
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: IEEE/SP
15th Workshop on Statistical Signal Processing (SSP 2009). ; 2009:257-260.
doi:10.1109/SSP.2009.5278589'
apa: Hennecke, M., Ploetz, T., Fink, G. A., Schmalenstroeer, J., & Haeb-Umbach,
R. (2009). A hierarchical approach to unsupervised shape calibration of microphone
array networks. IEEE/SP 15th Workshop on Statistical Signal Processing (SSP
2009), 257–260. https://doi.org/10.1109/SSP.2009.5278589
bibtex: '@inproceedings{Hennecke_Ploetz_Fink_Schmalenstroeer_Haeb-Umbach_2009, title={A
hierarchical approach to unsupervised shape calibration of microphone array networks},
DOI={10.1109/SSP.2009.5278589},
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} }'
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 IEEE/SP 15th Workshop on Statistical Signal
Processing (SSP 2009), 257–60, 2009. https://doi.org/10.1109/SSP.2009.5278589.
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 IEEE/SP 15th Workshop on Statistical Signal Processing (SSP 2009),
2009, pp. 257–260, doi: 10.1109/SSP.2009.5278589.'
mla: Hennecke, Marius, et al. “A Hierarchical Approach to Unsupervised Shape Calibration
of Microphone Array Networks.” IEEE/SP 15th Workshop on Statistical Signal
Processing (SSP 2009), 2009, pp. 257–60, doi:10.1109/SSP.2009.5278589.
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.'
date_created: 2019-07-12T05:28:37Z
date_updated: 2023-10-26T08:09:22Z
department:
- _id: '54'
doi: 10.1109/SSP.2009.5278589
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
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://groups.uni-paderborn.de/nt/pubs/2009/HePlFiScHa09.pdf
oa: '1'
page: 257-260
publication: IEEE/SP 15th Workshop on Statistical Signal Processing (SSP 2009)
quality_controlled: '1'
status: public
title: A hierarchical approach to unsupervised shape calibration of microphone array
networks
type: conference
user_id: '460'
year: '2009'
...