---
_id: '11922'
abstract:
- lang: eng
text: 'Besides the core learning algorithm itself, one major question in machine
learning is how to best encode given training data such that the learning technology
can efficiently learn based thereon and generalize to novel data. While classical
approaches often rely on a hand coded data representation, the topic of autonomous
representation or feature learning plays a major role in modern learning architectures.
The goal of this contribution is to give an overview about different principles
of autonomous feature learning, and to exemplify two principles based on two recent
examples: autonomous metric learning for sequences, and autonomous learning of
a deep representation for spoken language, respectively.'
author:
- first_name: Oliver
full_name: Walter, Oliver
last_name: Walter
- first_name: Reinhold
full_name: Haeb-Umbach, Reinhold
id: '242'
last_name: Haeb-Umbach
- first_name: Bassam
full_name: Mokbel, Bassam
last_name: Mokbel
- first_name: Benjamin
full_name: Paassen, Benjamin
last_name: Paassen
- first_name: Barbara
full_name: Hammer, Barbara
last_name: Hammer
citation:
ama: Walter O, Haeb-Umbach R, Mokbel B, Paassen B, Hammer B. Autonomous Learning
of Representations. KI - Kuenstliche Intelligenz. 2015:1-13. doi:http://dx.doi.org/10.1007/s13218-015-0372-1
apa: Walter, O., Haeb-Umbach, R., Mokbel, B., Paassen, B., & Hammer, B. (2015).
Autonomous Learning of Representations. KI - Kuenstliche Intelligenz, 1–13.
http://dx.doi.org/10.1007/s13218-015-0372-1
bibtex: '@article{Walter_Haeb-Umbach_Mokbel_Paassen_Hammer_2015, title={Autonomous
Learning of Representations}, DOI={http://dx.doi.org/10.1007/s13218-015-0372-1},
journal={KI - Kuenstliche Intelligenz}, author={Walter, Oliver and Haeb-Umbach,
Reinhold and Mokbel, Bassam and Paassen, Benjamin and Hammer, Barbara}, year={2015},
pages={1–13} }'
chicago: Walter, Oliver, Reinhold Haeb-Umbach, Bassam Mokbel, Benjamin Paassen,
and Barbara Hammer. “Autonomous Learning of Representations.” KI - Kuenstliche
Intelligenz, 2015, 1–13. http://dx.doi.org/10.1007/s13218-015-0372-1.
ieee: O. Walter, R. Haeb-Umbach, B. Mokbel, B. Paassen, and B. Hammer, “Autonomous
Learning of Representations,” KI - Kuenstliche Intelligenz, pp. 1–13, 2015.
mla: Walter, Oliver, et al. “Autonomous Learning of Representations.” KI - Kuenstliche
Intelligenz, 2015, pp. 1–13, doi:http://dx.doi.org/10.1007/s13218-015-0372-1.
short: O. Walter, R. Haeb-Umbach, B. Mokbel, B. Paassen, B. Hammer, KI - Kuenstliche
Intelligenz (2015) 1–13.
date_created: 2019-07-12T05:30:51Z
date_updated: 2022-01-06T06:51:12Z
department:
- _id: '54'
doi: http://dx.doi.org/10.1007/s13218-015-0372-1
keyword:
- Representation learning
- Metric learning
- Deep representation
- Spoken language
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://groups.uni-paderborn.de/nt/pubs/2015/WaHaMoPaHa15.pdf
oa: '1'
page: 1-13
publication: KI - Kuenstliche Intelligenz
status: public
title: Autonomous Learning of Representations
type: journal_article
user_id: '44006'
year: '2015'
...
---
_id: '11923'
abstract:
- lang: eng
text: 'In this paper we show that recently developed algorithms for unsupervised
word segmentation can be a valuable tool for the documentation of endangered languages.
We applied an unsupervised word segmentation algorithm based on a nested Pitman-Yor
language model to two austronesian languages, Wooi and Waima''a. The algorithm
was then modified and parameterized to cater the needs of linguists for high precision
of lexical discovery: We obtained a lexicon precision of of 69.2\% and 67.5\%
for Wooi and Waima''a, respectively, if single-letter words and words found less
than three times were discarded. A comparison with an English word segmentation
task showed comparable performance, verifying that the assumptions underlying
the Pitman-Yor language model, the universality of Zipf''s law and the power of
n-gram structures, do also hold for languages as exotic as Wooi and Waima''a.'
author:
- first_name: Oliver
full_name: Walter, Oliver
last_name: Walter
- first_name: Reinhold
full_name: Haeb-Umbach, Reinhold
id: '242'
last_name: Haeb-Umbach
- first_name: Jan
full_name: Strunk, Jan
last_name: Strunk
- first_name: 'Nikolaus '
full_name: 'P. Himmelmann, Nikolaus '
last_name: P. Himmelmann
citation:
ama: Walter O, Haeb-Umbach R, Strunk J, P. Himmelmann N. Lexicon Discovery for
Language Preservation Using Unsupervised Word Segmentation with Pitman-Yor Language
Models (FGNT-2015-01).; 2015.
apa: Walter, O., Haeb-Umbach, R., Strunk, J., & P. Himmelmann, N. (2015). Lexicon
Discovery for Language Preservation using Unsupervised Word Segmentation with
Pitman-Yor Language Models (FGNT-2015-01).
bibtex: '@book{Walter_Haeb-Umbach_Strunk_P. Himmelmann_2015, title={Lexicon Discovery
for Language Preservation using Unsupervised Word Segmentation with Pitman-Yor
Language Models (FGNT-2015-01)}, author={Walter, Oliver and Haeb-Umbach, Reinhold
and Strunk, Jan and P. Himmelmann, Nikolaus }, year={2015} }'
chicago: Walter, Oliver, Reinhold Haeb-Umbach, Jan Strunk, and Nikolaus P. Himmelmann.
Lexicon Discovery for Language Preservation Using Unsupervised Word Segmentation
with Pitman-Yor Language Models (FGNT-2015-01), 2015.
ieee: O. Walter, R. Haeb-Umbach, J. Strunk, and N. P. Himmelmann, Lexicon Discovery
for Language Preservation using Unsupervised Word Segmentation with Pitman-Yor
Language Models (FGNT-2015-01). 2015.
mla: Walter, Oliver, et al. Lexicon Discovery for Language Preservation Using
Unsupervised Word Segmentation with Pitman-Yor Language Models (FGNT-2015-01).
2015.
short: O. Walter, R. Haeb-Umbach, J. Strunk, N. P. Himmelmann, Lexicon Discovery
for Language Preservation Using Unsupervised Word Segmentation with Pitman-Yor
Language Models (FGNT-2015-01), 2015.
date_created: 2019-07-12T05:30:52Z
date_updated: 2022-01-06T06:51:12Z
department:
- _id: '54'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://groups.uni-paderborn.de/nt/pubs/2015/WaHaStHi.pdf
oa: '1'
status: public
title: Lexicon Discovery for Language Preservation using Unsupervised Word Segmentation
with Pitman-Yor Language Models (FGNT-2015-01)
type: report
user_id: '44006'
year: '2015'
...
---
_id: '11874'
author:
- first_name: Manh Kha
full_name: Hoang, Manh Kha
last_name: Hoang
- 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: 'Hoang MK, Schmalenstroeer J, Haeb-Umbach R. Aligning training models with
smartphone properties in WiFi fingerprinting based indoor localization. In: 40th
International Conference on Acoustics, Speech and Signal Processing (ICASSP 2015).
; 2015.'
apa: Hoang, M. K., Schmalenstroeer, J., & Haeb-Umbach, R. (2015). Aligning training
models with smartphone properties in WiFi fingerprinting based indoor localization.
40th International Conference on Acoustics, Speech and Signal Processing (ICASSP
2015).
bibtex: '@inproceedings{Hoang_Schmalenstroeer_Haeb-Umbach_2015, title={Aligning
training models with smartphone properties in WiFi fingerprinting based indoor
localization}, booktitle={40th International Conference on Acoustics, Speech and
Signal Processing (ICASSP 2015)}, author={Hoang, Manh Kha and Schmalenstroeer,
Joerg and Haeb-Umbach, Reinhold}, year={2015} }'
chicago: Hoang, Manh Kha, Joerg Schmalenstroeer, and Reinhold Haeb-Umbach. “Aligning
Training Models with Smartphone Properties in WiFi Fingerprinting Based Indoor
Localization.” In 40th International Conference on Acoustics, Speech and Signal
Processing (ICASSP 2015), 2015.
ieee: M. K. Hoang, J. Schmalenstroeer, and R. Haeb-Umbach, “Aligning training models
with smartphone properties in WiFi fingerprinting based indoor localization,”
2015.
mla: Hoang, Manh Kha, et al. “Aligning Training Models with Smartphone Properties
in WiFi Fingerprinting Based Indoor Localization.” 40th International Conference
on Acoustics, Speech and Signal Processing (ICASSP 2015), 2015.
short: 'M.K. Hoang, J. Schmalenstroeer, R. Haeb-Umbach, in: 40th International Conference
on Acoustics, Speech and Signal Processing (ICASSP 2015), 2015.'
date_created: 2019-07-12T05:29:55Z
date_updated: 2023-10-26T08:11:43Z
department:
- _id: '54'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://groups.uni-paderborn.de/nt/pubs/2015/HoSchHa2015.pdf
oa: '1'
publication: 40th International Conference on Acoustics, Speech and Signal Processing
(ICASSP 2015)
quality_controlled: '1'
status: public
title: Aligning training models with smartphone properties in WiFi fingerprinting
based indoor localization
type: conference
user_id: '460'
year: '2015'
...
---
_id: '11746'
abstract:
- lang: eng
text: ' "A method for nonstationary noise robust automatic speech recognition (ASR)
is to first estimate the changing noise statistics and second clean up the features
prior to recognition accordingly. Here, the first is accomplished by noise tracking
in the spectral domain, while the second relies on Bayesian enhancement in the
feature domain. In this way we take advantage of our recently proposed maximum
a-posteriori based (MAP-B) noise power spectral density estimation algorithm,
which is able to estimate the noise statistics even in time-frequency bins dominated
by speech. We show that MAP-B noise tracking leads to an improved noise model
estimate in the feature domain compared to estimating noise in speech absence
periods only, if the bias resulting from the nonlinear transformation from the
spectral to the feature domain is accounted for. Consequently, ASR results are
improved, as is shown by experiments conducted on the Aurora IV database." '
author:
- first_name: Aleksej
full_name: Chinaev, Aleksej
last_name: Chinaev
- first_name: Marc
full_name: Puels, Marc
last_name: Puels
- first_name: Reinhold
full_name: Haeb-Umbach, Reinhold
id: '242'
last_name: Haeb-Umbach
citation:
ama: 'Chinaev A, Puels M, Haeb-Umbach R. Spectral Noise Tracking for Improved Nonstationary
Noise Robust ASR. In: 11. ITG Fachtagung Sprachkommunikation (ITG 2014).
; 2014.'
apa: Chinaev, A., Puels, M., & Haeb-Umbach, R. (2014). Spectral Noise Tracking
for Improved Nonstationary Noise Robust ASR. In 11. ITG Fachtagung Sprachkommunikation
(ITG 2014).
bibtex: '@inproceedings{Chinaev_Puels_Haeb-Umbach_2014, title={Spectral Noise Tracking
for Improved Nonstationary Noise Robust ASR}, booktitle={11. ITG Fachtagung Sprachkommunikation
(ITG 2014)}, author={Chinaev, Aleksej and Puels, Marc and Haeb-Umbach, Reinhold},
year={2014} }'
chicago: Chinaev, Aleksej, Marc Puels, and Reinhold Haeb-Umbach. “Spectral Noise
Tracking for Improved Nonstationary Noise Robust ASR.” In 11. ITG Fachtagung
Sprachkommunikation (ITG 2014), 2014.
ieee: A. Chinaev, M. Puels, and R. Haeb-Umbach, “Spectral Noise Tracking for Improved
Nonstationary Noise Robust ASR,” in 11. ITG Fachtagung Sprachkommunikation
(ITG 2014), 2014.
mla: Chinaev, Aleksej, et al. “Spectral Noise Tracking for Improved Nonstationary
Noise Robust ASR.” 11. ITG Fachtagung Sprachkommunikation (ITG 2014), 2014.
short: 'A. Chinaev, M. Puels, R. Haeb-Umbach, in: 11. ITG Fachtagung Sprachkommunikation
(ITG 2014), 2014.'
date_created: 2019-07-12T05:27:27Z
date_updated: 2022-01-06T06:51:08Z
department:
- _id: '54'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://groups.uni-paderborn.de/nt/pubs/2014/ChPuHa2014.pdf
oa: '1'
publication: 11. ITG Fachtagung Sprachkommunikation (ITG 2014)
related_material:
link:
- description: Presentation
relation: supplementary_material
url: https://groups.uni-paderborn.de/nt/pubs/2014/ChPuHa2014_Talk.pdf
status: public
title: Spectral Noise Tracking for Improved Nonstationary Noise Robust ASR
type: conference
user_id: '44006'
year: '2014'
...
---
_id: '11752'
abstract:
- lang: eng
text: ' "In this contribution we derive a variational EM (VEM) algorithm for model
selection in complex Watson mixture models, which have been recently proposed
as a model of the distribution of normalized microphone array signals in the short-time
Fourier transform domain. The VEM algorithm is applied to count the number of
active sources in a speech mixture by iteratively estimating the mode vectors
of the Watson distributions and suppressing the signals from the corresponding
directions. A key theoretical contribution is the derivation of the MMSE estimate
of a quadratic form involving the mode vector of the Watson distribution. The
experimental results demonstrate the effectiveness of the source counting approach
at moderately low SNR. It is further shown that the VEM algorithm is more robust
w.r.t. used threshold values." '
author:
- first_name: Lukas
full_name: Drude, Lukas
id: '11213'
last_name: Drude
- first_name: Aleksej
full_name: Chinaev, Aleksej
last_name: Chinaev
- first_name: Dang Hai
full_name: Tran Vu, Dang Hai
last_name: Tran Vu
- first_name: Reinhold
full_name: Haeb-Umbach, Reinhold
id: '242'
last_name: Haeb-Umbach
citation:
ama: 'Drude L, Chinaev A, Tran Vu DH, Haeb-Umbach R. Source Counting in Speech Mixtures
Using a Variational EM Approach for Complexwatson Mixture Models. In: 39th
International Conference on Acoustics, Speech and Signal Processing (ICASSP 2014).
; 2014.'
apa: Drude, L., Chinaev, A., Tran Vu, D. H., & Haeb-Umbach, R. (2014). Source
Counting in Speech Mixtures Using a Variational EM Approach for Complexwatson
Mixture Models. In 39th International Conference on Acoustics, Speech and Signal
Processing (ICASSP 2014).
bibtex: '@inproceedings{Drude_Chinaev_Tran Vu_Haeb-Umbach_2014, title={Source Counting
in Speech Mixtures Using a Variational EM Approach for Complexwatson Mixture Models},
booktitle={39th International Conference on Acoustics, Speech and Signal Processing
(ICASSP 2014)}, author={Drude, Lukas and Chinaev, Aleksej and Tran Vu, Dang Hai
and Haeb-Umbach, Reinhold}, year={2014} }'
chicago: Drude, Lukas, Aleksej Chinaev, Dang Hai Tran Vu, and Reinhold Haeb-Umbach.
“Source Counting in Speech Mixtures Using a Variational EM Approach for Complexwatson
Mixture Models.” In 39th International Conference on Acoustics, Speech and
Signal Processing (ICASSP 2014), 2014.
ieee: L. Drude, A. Chinaev, D. H. Tran Vu, and R. Haeb-Umbach, “Source Counting
in Speech Mixtures Using a Variational EM Approach for Complexwatson Mixture Models,”
in 39th International Conference on Acoustics, Speech and Signal Processing
(ICASSP 2014), 2014.
mla: Drude, Lukas, et al. “Source Counting in Speech Mixtures Using a Variational
EM Approach for Complexwatson Mixture Models.” 39th International Conference
on Acoustics, Speech and Signal Processing (ICASSP 2014), 2014.
short: 'L. Drude, A. Chinaev, D.H. Tran Vu, R. Haeb-Umbach, in: 39th International
Conference on Acoustics, Speech and Signal Processing (ICASSP 2014), 2014.'
date_created: 2019-07-12T05:27:34Z
date_updated: 2022-01-06T06:51:08Z
department:
- _id: '54'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://groups.uni-paderborn.de/nt/pubs/2014/DrChTrHa2014.pdf
oa: '1'
publication: 39th International Conference on Acoustics, Speech and Signal Processing
(ICASSP 2014)
related_material:
link:
- description: Poster
relation: supplementary_material
url: https://groups.uni-paderborn.de/nt/pubs/2014/DrChTrHa2014_Poster.pdf
status: public
title: Source Counting in Speech Mixtures Using a Variational EM Approach for Complexwatson
Mixture Models
type: conference
user_id: '44006'
year: '2014'
...
---
_id: '11753'
abstract:
- lang: eng
text: This contribution describes a step-wise source counting algorithm to determine
the number of speakers in an offline scenario. Each speaker is identified by a
variational expectation maximization (VEM) algorithm for complex Watson mixture
models and therefore directly yields beamforming vectors for a subsequent speech
separation process. An observation selection criterion is proposed which improves
the robustness of the source counting in noise. The algorithm is compared to an
alternative VEM approach with Gaussian mixture models based on directions of arrival
and shown to deliver improved source counting accuracy. The article concludes
by extending the offline algorithm towards a low-latency online estimation of
the number of active sources from the streaming input data.
author:
- first_name: Lukas
full_name: Drude, Lukas
id: '11213'
last_name: Drude
- first_name: Aleksej
full_name: Chinaev, Aleksej
last_name: Chinaev
- first_name: Dang Hai
full_name: Tran Vu, Dang Hai
last_name: Tran Vu
- first_name: Reinhold
full_name: Haeb-Umbach, Reinhold
id: '242'
last_name: Haeb-Umbach
citation:
ama: 'Drude L, Chinaev A, Tran Vu DH, Haeb-Umbach R. Towards Online Source Counting
in Speech Mixtures Applying a Variational EM for Complex Watson Mixture Models.
In: 14th International Workshop on Acoustic Signal Enhancement (IWAENC 2014).
; 2014:213-217.'
apa: Drude, L., Chinaev, A., Tran Vu, D. H., & Haeb-Umbach, R. (2014). Towards
Online Source Counting in Speech Mixtures Applying a Variational EM for Complex
Watson Mixture Models. In 14th International Workshop on Acoustic Signal Enhancement
(IWAENC 2014) (pp. 213–217).
bibtex: '@inproceedings{Drude_Chinaev_Tran Vu_Haeb-Umbach_2014, title={Towards Online
Source Counting in Speech Mixtures Applying a Variational EM for Complex Watson
Mixture Models}, booktitle={14th International Workshop on Acoustic Signal Enhancement
(IWAENC 2014)}, author={Drude, Lukas and Chinaev, Aleksej and Tran Vu, Dang Hai
and Haeb-Umbach, Reinhold}, year={2014}, pages={213–217} }'
chicago: Drude, Lukas, Aleksej Chinaev, Dang Hai Tran Vu, and Reinhold Haeb-Umbach.
“Towards Online Source Counting in Speech Mixtures Applying a Variational EM for
Complex Watson Mixture Models.” In 14th International Workshop on Acoustic
Signal Enhancement (IWAENC 2014), 213–17, 2014.
ieee: L. Drude, A. Chinaev, D. H. Tran Vu, and R. Haeb-Umbach, “Towards Online Source
Counting in Speech Mixtures Applying a Variational EM for Complex Watson Mixture
Models,” in 14th International Workshop on Acoustic Signal Enhancement (IWAENC
2014), 2014, pp. 213–217.
mla: Drude, Lukas, et al. “Towards Online Source Counting in Speech Mixtures Applying
a Variational EM for Complex Watson Mixture Models.” 14th International Workshop
on Acoustic Signal Enhancement (IWAENC 2014), 2014, pp. 213–17.
short: 'L. Drude, A. Chinaev, D.H. Tran Vu, R. Haeb-Umbach, in: 14th International
Workshop on Acoustic Signal Enhancement (IWAENC 2014), 2014, pp. 213–217.'
date_created: 2019-07-12T05:27:35Z
date_updated: 2022-01-06T06:51:08Z
department:
- _id: '54'
keyword:
- Accuracy
- Acoustics
- Estimation
- Mathematical model
- Soruce separation
- Speech
- Vectors
- Bayes methods
- Blind source separation
- Directional statistics
- Number of speakers
- Speaker diarization
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://groups.uni-paderborn.de/nt/pubs/2014/DrChTrHaeb14.pdf
oa: '1'
page: 213-217
publication: 14th International Workshop on Acoustic Signal Enhancement (IWAENC 2014)
related_material:
link:
- description: Poster
relation: supplementary_material
url: https://groups.uni-paderborn.de/nt/pubs/2014/DrChTrHaeb14_Poster.pdf
status: public
title: Towards Online Source Counting in Speech Mixtures Applying a Variational EM
for Complex Watson Mixture Models
type: conference
user_id: '44006'
year: '2014'
...
---
_id: '11814'
abstract:
- lang: eng
text: ' "In this paper we present an algorithm for the unsupervised segmentation
of a lattice produced by a phoneme recognizer into words. Using a lattice rather
than a single phoneme string accounts for the uncertainty of the recognizer about
the true label sequence. An example application is the discovery of lexical units
from the output of an error-prone phoneme recognizer in a zero-resource setting,
where neither the lexicon nor the language model (LM) is known. We propose a computationally
efficient iterative approach, which alternates between the following two steps:
First, the most probable string is extracted from the lattice using a phoneme
LM learned on the segmentation result of the previous iteration. Second, word
segmentation is performed on the extracted string using a word and phoneme LM
which is learned alongside the new segmentation. We present results on lattices
produced by a phoneme recognizer on the WSJCAM0 dataset. We show that our approach
delivers superior segmentation performance than an earlier approach found in the
literature, in particular for higher-order language models. " '
author:
- first_name: Jahn
full_name: Heymann, Jahn
id: '9168'
last_name: Heymann
- first_name: Oliver
full_name: Walter, Oliver
last_name: Walter
- first_name: Reinhold
full_name: Haeb-Umbach, Reinhold
id: '242'
last_name: Haeb-Umbach
- first_name: Bhiksha
full_name: Raj, Bhiksha
last_name: Raj
citation:
ama: 'Heymann J, Walter O, Haeb-Umbach R, Raj B. Iterative Bayesian Word Segmentation
for Unspuervised Vocabulary Discovery from Phoneme Lattices. In: 39th International
Conference on Acoustics, Speech and Signal Processing (ICASSP 2014). ; 2014.'
apa: Heymann, J., Walter, O., Haeb-Umbach, R., & Raj, B. (2014). Iterative Bayesian
Word Segmentation for Unspuervised Vocabulary Discovery from Phoneme Lattices.
In 39th International Conference on Acoustics, Speech and Signal Processing
(ICASSP 2014).
bibtex: '@inproceedings{Heymann_Walter_Haeb-Umbach_Raj_2014, title={Iterative Bayesian
Word Segmentation for Unspuervised Vocabulary Discovery from Phoneme Lattices},
booktitle={39th International Conference on Acoustics, Speech and Signal Processing
(ICASSP 2014)}, author={Heymann, Jahn and Walter, Oliver and Haeb-Umbach, Reinhold
and Raj, Bhiksha}, year={2014} }'
chicago: Heymann, Jahn, Oliver Walter, Reinhold Haeb-Umbach, and Bhiksha Raj. “Iterative
Bayesian Word Segmentation for Unspuervised Vocabulary Discovery from Phoneme
Lattices.” In 39th International Conference on Acoustics, Speech and Signal
Processing (ICASSP 2014), 2014.
ieee: J. Heymann, O. Walter, R. Haeb-Umbach, and B. Raj, “Iterative Bayesian Word
Segmentation for Unspuervised Vocabulary Discovery from Phoneme Lattices,” in
39th International Conference on Acoustics, Speech and Signal Processing (ICASSP
2014), 2014.
mla: Heymann, Jahn, et al. “Iterative Bayesian Word Segmentation for Unspuervised
Vocabulary Discovery from Phoneme Lattices.” 39th International Conference
on Acoustics, Speech and Signal Processing (ICASSP 2014), 2014.
short: 'J. Heymann, O. Walter, R. Haeb-Umbach, B. Raj, in: 39th International Conference
on Acoustics, Speech and Signal Processing (ICASSP 2014), 2014.'
date_created: 2019-07-12T05:28:46Z
date_updated: 2022-01-06T06:51:09Z
department:
- _id: '54'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://groups.uni-paderborn.de/nt/pubs/2014/HeWaHa2014.pdf
oa: '1'
publication: 39th International Conference on Acoustics, Speech and Signal Processing
(ICASSP 2014)
related_material:
link:
- description: Poster
relation: supplementary_material
url: https://groups.uni-paderborn.de/nt/pubs/2014/HeWaHa2014_Poster.pdf
status: public
title: Iterative Bayesian Word Segmentation for Unspuervised Vocabulary Discovery
from Phoneme Lattices
type: conference
user_id: '44006'
year: '2014'
...
---
_id: '11831'
abstract:
- lang: eng
text: ' "Several self-localization algorithms have been proposed, that determine
the positions of either acoustic or visual sensors autonomously. Usually these
positions are given in a modality specific coordinate system, with an unknown
rotation, translation and scale between the different systems. For a joint audiovisual
tracking, where the different modalities support each other, the two modalities
need to be mapped into a common coordinate system. In this paper we propose to
estimate this mapping based on audiovisual correlates, i.e., a speaker that can
be localized by both, a microphone and a camera network separately. The voice
is tracked by a microphone network, which had to be calibrated by a self-localization
algorithm at first, and the head is tracked by a calibrated camera network. Unlike
existing Singular Value Decomposition based approaches to estimate the coordinate
system mapping, we propose to perform an estimation in the shape domain, which
turns out to be computationally more efficient. Simulations of the self-localization
of an acoustic sensor network and a following coordinate mapping for a joint speaker
localization showed a significant improvement of the localization performance,
since the modalities were able to support each other." '
author:
- first_name: Florian
full_name: Jacob, Florian
last_name: Jacob
- first_name: Reinhold
full_name: Haeb-Umbach, Reinhold
id: '242'
last_name: Haeb-Umbach
citation:
ama: 'Jacob F, Haeb-Umbach R. Coordinate Mapping Between an Acoustic and Visual
Sensor Network in the Shape Domain for a Joint Self-Calibrating Speaker Tracking.
In: 11. ITG Fachtagung Sprachkommunikation (ITG 2014). ; 2014.'
apa: Jacob, F., & Haeb-Umbach, R. (2014). Coordinate Mapping Between an Acoustic
and Visual Sensor Network in the Shape Domain for a Joint Self-Calibrating Speaker
Tracking. In 11. ITG Fachtagung Sprachkommunikation (ITG 2014).
bibtex: '@inproceedings{Jacob_Haeb-Umbach_2014, title={Coordinate Mapping Between
an Acoustic and Visual Sensor Network in the Shape Domain for a Joint Self-Calibrating
Speaker Tracking}, booktitle={11. ITG Fachtagung Sprachkommunikation (ITG 2014)},
author={Jacob, Florian and Haeb-Umbach, Reinhold}, year={2014} }'
chicago: Jacob, Florian, and Reinhold Haeb-Umbach. “Coordinate Mapping Between an
Acoustic and Visual Sensor Network in the Shape Domain for a Joint Self-Calibrating
Speaker Tracking.” In 11. ITG Fachtagung Sprachkommunikation (ITG 2014),
2014.
ieee: F. Jacob and R. Haeb-Umbach, “Coordinate Mapping Between an Acoustic and Visual
Sensor Network in the Shape Domain for a Joint Self-Calibrating Speaker Tracking,”
in 11. ITG Fachtagung Sprachkommunikation (ITG 2014), 2014.
mla: Jacob, Florian, and Reinhold Haeb-Umbach. “Coordinate Mapping Between an Acoustic
and Visual Sensor Network in the Shape Domain for a Joint Self-Calibrating Speaker
Tracking.” 11. ITG Fachtagung Sprachkommunikation (ITG 2014), 2014.
short: 'F. Jacob, R. Haeb-Umbach, in: 11. ITG Fachtagung Sprachkommunikation (ITG
2014), 2014.'
date_created: 2019-07-12T05:29:06Z
date_updated: 2022-01-06T06:51:11Z
department:
- _id: '54'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://groups.uni-paderborn.de/nt/pubs/2014/JaHa2014.pdf
oa: '1'
publication: 11. ITG Fachtagung Sprachkommunikation (ITG 2014)
related_material:
link:
- description: Presentation
relation: supplementary_material
url: https://groups.uni-paderborn.de/nt/pubs/2014/JaHa2014_Talk.pdf
status: public
title: Coordinate Mapping Between an Acoustic and Visual Sensor Network in the Shape
Domain for a Joint Self-Calibrating Speaker Tracking
type: conference
user_id: '44006'
year: '2014'
...
---
_id: '11861'
abstract:
- lang: eng
text: 'In this contribution we present a theoretical and experimental investigation
into the effects of reverberation and noise on features in the logarithmic mel
power spectral domain, an intermediate stage in the computation of the mel frequency
cepstral coefficients, prevalent in automatic speech recognition (ASR). Gaining
insight into the complex interaction between clean speech, noise, and noisy reverberant
speech features is essential for any ASR system to be robust against noise and
reverberation present in distant microphone input signals. The findings are gathered
in a probabilistic formulation of an observation model which may be used in model-based
feature compensation schemes. The proposed observation model extends previous
models in three major directions: First, the contribution of additive background
noise to the observation error is explicitly taken into account. Second, an energy
compensation constant is introduced which ensures an unbiased estimate of the
reverberant speech features, and, third, a recursive variant of the observation
model is developed resulting in reduced computational complexity when used in
model-based feature compensation. The experimental section is used to evaluate
the accuracy of the model and to describe how its parameters can be determined
from test data.'
author:
- first_name: Volker
full_name: Leutnant, Volker
last_name: Leutnant
- first_name: Alexander
full_name: Krueger, Alexander
last_name: Krueger
- first_name: Reinhold
full_name: Haeb-Umbach, Reinhold
id: '242'
last_name: Haeb-Umbach
citation:
ama: Leutnant V, Krueger A, Haeb-Umbach R. A New Observation Model in the Logarithmic
Mel Power Spectral Domain for the Automatic Recognition of Noisy Reverberant Speech.
IEEE/ACM Transactions on Audio, Speech, and Language Processing. 2014;22(1):95-109.
doi:10.1109/TASLP.2013.2285480
apa: Leutnant, V., Krueger, A., & Haeb-Umbach, R. (2014). A New Observation
Model in the Logarithmic Mel Power Spectral Domain for the Automatic Recognition
of Noisy Reverberant Speech. IEEE/ACM Transactions on Audio, Speech, and Language
Processing, 22(1), 95–109. https://doi.org/10.1109/TASLP.2013.2285480
bibtex: '@article{Leutnant_Krueger_Haeb-Umbach_2014, title={A New Observation Model
in the Logarithmic Mel Power Spectral Domain for the Automatic Recognition of
Noisy Reverberant Speech}, volume={22}, DOI={10.1109/TASLP.2013.2285480},
number={1}, journal={IEEE/ACM Transactions on Audio, Speech, and Language Processing},
author={Leutnant, Volker and Krueger, Alexander and Haeb-Umbach, Reinhold}, year={2014},
pages={95–109} }'
chicago: 'Leutnant, Volker, Alexander Krueger, and Reinhold Haeb-Umbach. “A New
Observation Model in the Logarithmic Mel Power Spectral Domain for the Automatic
Recognition of Noisy Reverberant Speech.” IEEE/ACM Transactions on Audio, Speech,
and Language Processing 22, no. 1 (2014): 95–109. https://doi.org/10.1109/TASLP.2013.2285480.'
ieee: V. Leutnant, A. Krueger, and R. Haeb-Umbach, “A New Observation Model in the
Logarithmic Mel Power Spectral Domain for the Automatic Recognition of Noisy Reverberant
Speech,” IEEE/ACM Transactions on Audio, Speech, and Language Processing,
vol. 22, no. 1, pp. 95–109, 2014.
mla: Leutnant, Volker, et al. “A New Observation Model in the Logarithmic Mel Power
Spectral Domain for the Automatic Recognition of Noisy Reverberant Speech.” IEEE/ACM
Transactions on Audio, Speech, and Language Processing, vol. 22, no. 1, 2014,
pp. 95–109, doi:10.1109/TASLP.2013.2285480.
short: V. Leutnant, A. Krueger, R. Haeb-Umbach, IEEE/ACM Transactions on Audio,
Speech, and Language Processing 22 (2014) 95–109.
date_created: 2019-07-12T05:29:41Z
date_updated: 2022-01-06T06:51:11Z
department:
- _id: '54'
doi: 10.1109/TASLP.2013.2285480
intvolume: ' 22'
issue: '1'
keyword:
- computational complexity
- reverberation
- speech recognition
- automatic speech recognition
- background noise
- clean speech
- computational complexity
- energy compensation
- logarithmic mel power spectral domain
- mel frequency cepstral coefficients
- microphone input signals
- model-based feature compensation schemes
- noisy reverberant speech automatic recognition
- noisy reverberant speech features
- reverberation
- Atmospheric modeling
- Computational modeling
- Noise
- Noise measurement
- Reverberation
- Speech
- Vectors
- Model-based feature compensation
- observation model for reverberant and noisy speech
- recursive observation model
- robust automatic speech recognition
language:
- iso: eng
page: 95-109
publication: IEEE/ACM Transactions on Audio, Speech, and Language Processing
publication_identifier:
issn:
- 2329-9290
status: public
title: A New Observation Model in the Logarithmic Mel Power Spectral Domain for the
Automatic Recognition of Noisy Reverberant Speech
type: journal_article
user_id: '44006'
volume: 22
year: '2014'
...
---
_id: '11867'
abstract:
- lang: eng
text: 'New waves of consumer-centric applications, such as voice search and voice
interaction with mobile devices and home entertainment systems, increasingly require
automatic speech recognition (ASR) to be robust to the full range of real-world
noise and other acoustic distorting conditions. Despite its practical importance,
however, the inherent links between and distinctions among the myriad of methods
for noise-robust ASR have yet to be carefully studied in order to advance the
field further. To this end, it is critical to establish a solid, consistent, and
common mathematical foundation for noise-robust ASR, which is lacking at present.
This article is intended to fill this gap and to provide a thorough overview of
modern noise-robust techniques for ASR developed over the past 30 years. We emphasize
methods that are proven to be successful and that are likely to sustain or expand
their future applicability. We distill key insights from our comprehensive overview
in this field and take a fresh look at a few old problems, which nevertheless
are still highly relevant today. Specifically, we have analyzed and categorized
a wide range of noise-robust techniques using five different criteria: 1) feature-domain
vs. model-domain processing, 2) the use of prior knowledge about the acoustic
environment distortion, 3) the use of explicit environment-distortion models,
4) deterministic vs. uncertainty processing, and 5) the use of acoustic models
trained jointly with the same feature enhancement or model adaptation process
used in the testing stage. With this taxonomy-oriented review, we equip the reader
with the insight to choose among techniques and with the awareness of the performance-complexity
tradeoffs. The pros and cons of using different noise-robust ASR techniques in
practical application scenarios are provided as a guide to interested practitioners.
The current challenges and future research directions in this field is also carefully
analyzed.'
author:
- first_name: Jinyu
full_name: Li, Jinyu
last_name: Li
- first_name: Li
full_name: Deng, Li
last_name: Deng
- first_name: Yifan
full_name: Gong, Yifan
last_name: Gong
- first_name: Reinhold
full_name: Haeb-Umbach, Reinhold
id: '242'
last_name: Haeb-Umbach
citation:
ama: Li J, Deng L, Gong Y, Haeb-Umbach R. An Overview of Noise-Robust Automatic
Speech Recognition. IEEE Transactions on Audio, Speech and Language Processing.
2014;22(4):745-777. doi:10.1109/TASLP.2014.2304637
apa: Li, J., Deng, L., Gong, Y., & Haeb-Umbach, R. (2014). An Overview of Noise-Robust
Automatic Speech Recognition. IEEE Transactions on Audio, Speech and Language
Processing, 22(4), 745–777. https://doi.org/10.1109/TASLP.2014.2304637
bibtex: '@article{Li_Deng_Gong_Haeb-Umbach_2014, title={An Overview of Noise-Robust
Automatic Speech Recognition}, volume={22}, DOI={10.1109/TASLP.2014.2304637},
number={4}, journal={IEEE Transactions on Audio, Speech and Language Processing},
author={Li, Jinyu and Deng, Li and Gong, Yifan and Haeb-Umbach, Reinhold}, year={2014},
pages={745–777} }'
chicago: 'Li, Jinyu, Li Deng, Yifan Gong, and Reinhold Haeb-Umbach. “An Overview
of Noise-Robust Automatic Speech Recognition.” IEEE Transactions on Audio,
Speech and Language Processing 22, no. 4 (2014): 745–77. https://doi.org/10.1109/TASLP.2014.2304637.'
ieee: J. Li, L. Deng, Y. Gong, and R. Haeb-Umbach, “An Overview of Noise-Robust
Automatic Speech Recognition,” IEEE Transactions on Audio, Speech and Language
Processing, vol. 22, no. 4, pp. 745–777, 2014.
mla: Li, Jinyu, et al. “An Overview of Noise-Robust Automatic Speech Recognition.”
IEEE Transactions on Audio, Speech and Language Processing, vol. 22, no.
4, 2014, pp. 745–77, doi:10.1109/TASLP.2014.2304637.
short: J. Li, L. Deng, Y. Gong, R. Haeb-Umbach, IEEE Transactions on Audio, Speech
and Language Processing 22 (2014) 745–777.
date_created: 2019-07-12T05:29:47Z
date_updated: 2022-01-06T06:51:11Z
department:
- _id: '54'
doi: 10.1109/TASLP.2014.2304637
intvolume: ' 22'
issue: '4'
keyword:
- Speech recognition
- compensation
- distortion modeling
- joint model training
- noise
- robustness
- uncertainty processing
language:
- iso: eng
main_file_link:
- open_access: '1'
url: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6732927
oa: '1'
page: 745-777
publication: IEEE Transactions on Audio, Speech and Language Processing
status: public
title: An Overview of Noise-Robust Automatic Speech Recognition
type: journal_article
user_id: '44006'
volume: 22
year: '2014'
...
---
_id: '11918'
abstract:
- lang: eng
text: 'In this paper, we investigate unsupervised acoustic model training approaches
for dysarthric-speech recognition. These models are first, frame-based Gaussian
posteriorgrams, obtained from Vector Quantization (VQ), second, so-called Acoustic
Unit Descriptors (AUDs), which are hidden Markov models of phone-like units, that
are trained in an unsupervised fashion, and, third, posteriorgrams computed on
the AUDs. Experiments were carried out on a database collected from a home automation
task and containing nine speakers, of which seven are considered to utter dysarthric
speech. All unsupervised modeling approaches delivered significantly better recognition
rates than a speaker-independent phoneme recognition baseline, showing the suitability
of unsupervised acoustic model training for dysarthric speech. While the AUD models
led to the most compact representation of an utterance for the subsequent semantic
inference stage, posteriorgram-based representations resulted in higher recognition
rates, with the Gaussian posteriorgram achieving the highest slot filling F-score
of 97.02%. Index Terms: unsupervised learning, acoustic unit descriptors, dysarthric
speech, non-negative matrix factorization'
author:
- first_name: Oliver
full_name: Walter, Oliver
last_name: Walter
- first_name: Vladimir
full_name: Despotovic, Vladimir
last_name: Despotovic
- first_name: Reinhold
full_name: Haeb-Umbach, Reinhold
id: '242'
last_name: Haeb-Umbach
- first_name: Jrt
full_name: Gemmeke, Jrt
last_name: Gemmeke
- first_name: Bart
full_name: Ons, Bart
last_name: Ons
- first_name: Hugo
full_name: Van hamme, Hugo
last_name: Van hamme
citation:
ama: 'Walter O, Despotovic V, Haeb-Umbach R, Gemmeke J, Ons B, Van hamme H. An Evaluation
of Unsupervised Acoustic Model Training for a Dysarthric Speech Interface. In:
INTERSPEECH 2014. ; 2014.'
apa: Walter, O., Despotovic, V., Haeb-Umbach, R., Gemmeke, J., Ons, B., & Van
hamme, H. (2014). An Evaluation of Unsupervised Acoustic Model Training for a
Dysarthric Speech Interface. In INTERSPEECH 2014.
bibtex: '@inproceedings{Walter_Despotovic_Haeb-Umbach_Gemmeke_Ons_Van hamme_2014,
title={An Evaluation of Unsupervised Acoustic Model Training for a Dysarthric
Speech Interface}, booktitle={INTERSPEECH 2014}, author={Walter, Oliver and Despotovic,
Vladimir and Haeb-Umbach, Reinhold and Gemmeke, Jrt and Ons, Bart and Van hamme,
Hugo}, year={2014} }'
chicago: Walter, Oliver, Vladimir Despotovic, Reinhold Haeb-Umbach, Jrt Gemmeke,
Bart Ons, and Hugo Van hamme. “An Evaluation of Unsupervised Acoustic Model Training
for a Dysarthric Speech Interface.” In INTERSPEECH 2014, 2014.
ieee: O. Walter, V. Despotovic, R. Haeb-Umbach, J. Gemmeke, B. Ons, and H. Van hamme,
“An Evaluation of Unsupervised Acoustic Model Training for a Dysarthric Speech
Interface,” in INTERSPEECH 2014, 2014.
mla: Walter, Oliver, et al. “An Evaluation of Unsupervised Acoustic Model Training
for a Dysarthric Speech Interface.” INTERSPEECH 2014, 2014.
short: 'O. Walter, V. Despotovic, R. Haeb-Umbach, J. Gemmeke, B. Ons, H. Van hamme,
in: INTERSPEECH 2014, 2014.'
date_created: 2019-07-12T05:30:46Z
date_updated: 2022-01-06T06:51:12Z
department:
- _id: '54'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://groups.uni-paderborn.de/nt/pubs/2014/WaDeHaebGeOnVa14.pdf
oa: '1'
publication: INTERSPEECH 2014
related_material:
link:
- description: Poster
relation: supplementary_material
url: https://groups.uni-paderborn.de/nt/pubs/2014/WaDeHaebGeOnVa14_Poster.pdf
- description: Spotlight
relation: supplementary_material
url: https://groups.uni-paderborn.de/nt/pubs/2014/WaDeHaebGeOnVa14_Spotlight.pdf
status: public
title: An Evaluation of Unsupervised Acoustic Model Training for a Dysarthric Speech
Interface
type: conference
user_id: '44006'
year: '2014'
...
---
_id: '11898'
abstract:
- lang: eng
text: Abstract In this paper we present an approach for synchronizing a wireless
acoustic sensor network using a two-stage procedure. First the clock frequency
and phase differences between pairs of nodes are estimated employing a two-way
message exchange protocol. The estimates are further improved in a Kalman filter
with a dedicated observation error model. In the second stage network-wide synchronization
is achieved by means of a gossiping algorithm which estimates the average clock
frequency and phase of the sensor nodes. These averages are viewed as frequency
and phase of a virtual master clock, to which the clocks of the sensor nodes have
to be adjusted. The amount of adjustment is computed in a specific control loop.
While these steps are done in software, the actual sampling rate correction is
carried out in hardware by using an adjustable frequency synthesizer. Experimental
results obtained from hardware devices and software simulations of large scale
networks are presented.
author:
- first_name: Joerg
full_name: Schmalenstroeer, Joerg
id: '460'
last_name: Schmalenstroeer
- first_name: Patrick
full_name: Jebramcik, Patrick
last_name: Jebramcik
- first_name: Reinhold
full_name: Haeb-Umbach, Reinhold
id: '242'
last_name: Haeb-Umbach
citation:
ama: Schmalenstroeer J, Jebramcik P, Haeb-Umbach R. A combined hardware-software
approach for acoustic sensor network synchronization . Signal Processing.
2014;(0). doi:http://dx.doi.org/10.1016/j.sigpro.2014.06.030
apa: Schmalenstroeer, J., Jebramcik, P., & Haeb-Umbach, R. (2014). A combined
hardware-software approach for acoustic sensor network synchronization . Signal
Processing, 0. http://dx.doi.org/10.1016/j.sigpro.2014.06.030
bibtex: '@article{Schmalenstroeer_Jebramcik_Haeb-Umbach_2014, title={A combined
hardware-software approach for acoustic sensor network synchronization }, DOI={http://dx.doi.org/10.1016/j.sigpro.2014.06.030},
number={0}, journal={Signal Processing}, author={Schmalenstroeer, Joerg and Jebramcik,
Patrick and Haeb-Umbach, Reinhold}, year={2014} }'
chicago: Schmalenstroeer, Joerg, Patrick Jebramcik, and Reinhold Haeb-Umbach. “A
Combined Hardware-Software Approach for Acoustic Sensor Network Synchronization
.” Signal Processing, no. 0 (2014). http://dx.doi.org/10.1016/j.sigpro.2014.06.030.
ieee: 'J. Schmalenstroeer, P. Jebramcik, and R. Haeb-Umbach, “A combined hardware-software
approach for acoustic sensor network synchronization ,” Signal Processing,
no. 0, p., 2014, doi: http://dx.doi.org/10.1016/j.sigpro.2014.06.030.'
mla: Schmalenstroeer, Joerg, et al. “A Combined Hardware-Software Approach for Acoustic
Sensor Network Synchronization .” Signal Processing, no. 0, 2014, p., doi:http://dx.doi.org/10.1016/j.sigpro.2014.06.030.
short: J. Schmalenstroeer, P. Jebramcik, R. Haeb-Umbach, Signal Processing (2014).
date_created: 2019-07-12T05:30:23Z
date_updated: 2023-10-26T08:11:22Z
department:
- _id: '54'
doi: http://dx.doi.org/10.1016/j.sigpro.2014.06.030
issue: '0'
keyword:
- Gossip algorithm
language:
- iso: eng
main_file_link:
- open_access: '1'
url: http://www.sciencedirect.com/science/article/pii/S0165168414002990
oa: '1'
page: ' - '
publication: Signal Processing
publication_identifier:
issn:
- 0165-1684
quality_controlled: '1'
status: public
title: 'A combined hardware-software approach for acoustic sensor network synchronization '
type: journal_article
user_id: '460'
year: '2014'
...
---
_id: '11897'
abstract:
- lang: eng
text: ' "In this paper we present an approach for synchronizing the sampling clocks
of distributed microphones over a wireless network. The proposed system uses a
two stage procedure. It first employs a two-way message exchange algorithm to
estimate the clock phase and frequency difference between two nodes and then uses
a gossiping algorithmto estimate a virtual master clock, to which all sensor nodes
synchronize. Simulation results are presented for networks of different topology
and size, showing the effectiveness of our approach." '
author:
- first_name: Joerg
full_name: Schmalenstroeer, Joerg
id: '460'
last_name: Schmalenstroeer
- first_name: Patrick
full_name: Jebramcik, Patrick
last_name: Jebramcik
- first_name: Reinhold
full_name: Haeb-Umbach, Reinhold
id: '242'
last_name: Haeb-Umbach
citation:
ama: 'Schmalenstroeer J, Jebramcik P, Haeb-Umbach R. A Gossiping Approach to Sampling
Clock Synchronization in Wireless Acoustic Sensor Networks. In: 39th International
Conference on Acoustics, Speech and Signal Processing (ICASSP 2014). ; 2014.'
apa: Schmalenstroeer, J., Jebramcik, P., & Haeb-Umbach, R. (2014). A Gossiping
Approach to Sampling Clock Synchronization in Wireless Acoustic Sensor Networks.
39th International Conference on Acoustics, Speech and Signal Processing (ICASSP
2014).
bibtex: '@inproceedings{Schmalenstroeer_Jebramcik_Haeb-Umbach_2014, title={A Gossiping
Approach to Sampling Clock Synchronization in Wireless Acoustic Sensor Networks},
booktitle={39th International Conference on Acoustics, Speech and Signal Processing
(ICASSP 2014)}, author={Schmalenstroeer, Joerg and Jebramcik, Patrick and Haeb-Umbach,
Reinhold}, year={2014} }'
chicago: Schmalenstroeer, Joerg, Patrick Jebramcik, and Reinhold Haeb-Umbach. “A
Gossiping Approach to Sampling Clock Synchronization in Wireless Acoustic Sensor
Networks.” In 39th International Conference on Acoustics, Speech and Signal
Processing (ICASSP 2014), 2014.
ieee: J. Schmalenstroeer, P. Jebramcik, and R. Haeb-Umbach, “A Gossiping Approach
to Sampling Clock Synchronization in Wireless Acoustic Sensor Networks,” 2014.
mla: Schmalenstroeer, Joerg, et al. “A Gossiping Approach to Sampling Clock Synchronization
in Wireless Acoustic Sensor Networks.” 39th International Conference on Acoustics,
Speech and Signal Processing (ICASSP 2014), 2014.
short: 'J. Schmalenstroeer, P. Jebramcik, R. Haeb-Umbach, in: 39th International
Conference on Acoustics, Speech and Signal Processing (ICASSP 2014), 2014.'
date_created: 2019-07-12T05:30:22Z
date_updated: 2023-10-26T08:11:31Z
department:
- _id: '54'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://groups.uni-paderborn.de/nt/pubs/2014/SchHae2014.pdf
oa: '1'
publication: 39th International Conference on Acoustics, Speech and Signal Processing
(ICASSP 2014)
quality_controlled: '1'
related_material:
link:
- description: Poster
relation: supplementary_material
url: https://groups.uni-paderborn.de/nt/pubs/2014/SchHaebICASSP2014_Poster.pdf
status: public
title: A Gossiping Approach to Sampling Clock Synchronization in Wireless Acoustic
Sensor Networks
type: conference
user_id: '460'
year: '2014'
...
---
_id: '11903'
abstract:
- lang: eng
text: '"Acoustic sensor network clock synchronization via time stamp exchange between
the sensor nodes is not accurate enough for many acoustic signal processing tasks,
such as speaker localization. To improve synchronization accuracy it has therefore
been proposed to employ a Kalman Filter to obtain improved frequency deviation
and phase offset estimates. The estimation requires a statistical model of the
errors of the measurements obtained from the time stamp exchange algorithm. These
errors are caused by random transmission delays and hardware effects and are thus
network specific. In this contribution we develop an algorithm to estimate the
parameters of the measurement error model alongside the Kalman filter based sampling
clock synchronization, employing the Expectation Maximization algorithm. Simulation
results demonstrate that the online estimation of the error model parameters leads
only to a small degradation of the synchronization performance compared to a perfectly
known observation error model."'
author:
- first_name: Joerg
full_name: Schmalenstroeer, Joerg
id: '460'
last_name: Schmalenstroeer
- first_name: Weile
full_name: Zhao, Weile
last_name: Zhao
- first_name: Reinhold
full_name: Haeb-Umbach, Reinhold
id: '242'
last_name: Haeb-Umbach
citation:
ama: 'Schmalenstroeer J, Zhao W, Haeb-Umbach R. Online Observation Error Model Estimation
for Acoustic Sensor Network Synchronization. In: 11. ITG Fachtagung Sprachkommunikation
(ITG 2014). ; 2014.'
apa: Schmalenstroeer, J., Zhao, W., & Haeb-Umbach, R. (2014). Online Observation
Error Model Estimation for Acoustic Sensor Network Synchronization. 11. ITG
Fachtagung Sprachkommunikation (ITG 2014).
bibtex: '@inproceedings{Schmalenstroeer_Zhao_Haeb-Umbach_2014, title={Online Observation
Error Model Estimation for Acoustic Sensor Network Synchronization}, booktitle={11.
ITG Fachtagung Sprachkommunikation (ITG 2014)}, author={Schmalenstroeer, Joerg
and Zhao, Weile and Haeb-Umbach, Reinhold}, year={2014} }'
chicago: Schmalenstroeer, Joerg, Weile Zhao, and Reinhold Haeb-Umbach. “Online Observation
Error Model Estimation for Acoustic Sensor Network Synchronization.” In 11.
ITG Fachtagung Sprachkommunikation (ITG 2014), 2014.
ieee: J. Schmalenstroeer, W. Zhao, and R. Haeb-Umbach, “Online Observation Error
Model Estimation for Acoustic Sensor Network Synchronization,” 2014.
mla: Schmalenstroeer, Joerg, et al. “Online Observation Error Model Estimation for
Acoustic Sensor Network Synchronization.” 11. ITG Fachtagung Sprachkommunikation
(ITG 2014), 2014.
short: 'J. Schmalenstroeer, W. Zhao, R. Haeb-Umbach, in: 11. ITG Fachtagung Sprachkommunikation
(ITG 2014), 2014.'
date_created: 2019-07-12T05:30:29Z
date_updated: 2023-10-26T08:14:00Z
department:
- _id: '54'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://groups.uni-paderborn.de/nt/pubs/2014/SchHaebITG2014.pdf
oa: '1'
publication: 11. ITG Fachtagung Sprachkommunikation (ITG 2014)
quality_controlled: '1'
related_material:
link:
- description: Poster
relation: supplementary_material
url: https://groups.uni-paderborn.de/nt/pubs/2014/SchHaebITG2014_Poster.pdf
- description: Demo
relation: supplementary_material
url: https://groups.uni-paderborn.de/nt/pubs/2014/SchHaebITG2014_Demo.pdf
status: public
title: Online Observation Error Model Estimation for Acoustic Sensor Network Synchronization
type: conference
user_id: '460'
year: '2014'
...
---
_id: '11716'
abstract:
- lang: eng
text: The accuracy of automatic speech recognition systems in noisy and reverberant
environments can be improved notably by exploiting the uncertainty of the estimated
speech features using so-called uncertainty-of-observation techniques. In this
paper, we introduce a new Bayesian decision rule that can serve as a mathematical
framework from which both known and new uncertainty-of-observation techniques
can be either derived or approximated. The new decision rule in its direct form
leads to the new significance decoding approach for Gaussian mixture models, which
results in better performance compared to standard uncertainty-of-observation
techniques in different additive and convolutive noise scenarios.
author:
- first_name: Ahmed H.
full_name: Abdelaziz, Ahmed H.
last_name: Abdelaziz
- first_name: Steffen
full_name: Zeiler, Steffen
last_name: Zeiler
- first_name: Dorothea
full_name: Kolossa, Dorothea
last_name: Kolossa
- first_name: Volker
full_name: Leutnant, Volker
last_name: Leutnant
- first_name: Reinhold
full_name: Haeb-Umbach, Reinhold
id: '242'
last_name: Haeb-Umbach
citation:
ama: 'Abdelaziz AH, Zeiler S, Kolossa D, Leutnant V, Haeb-Umbach R. GMM-based significance
decoding. In: Acoustics, Speech and Signal Processing (ICASSP), 2013 IEEE International
Conference On. ; 2013:6827-6831. doi:10.1109/ICASSP.2013.6638984'
apa: Abdelaziz, A. H., Zeiler, S., Kolossa, D., Leutnant, V., & Haeb-Umbach,
R. (2013). GMM-based significance decoding. In Acoustics, Speech and Signal
Processing (ICASSP), 2013 IEEE International Conference on (pp. 6827–6831).
https://doi.org/10.1109/ICASSP.2013.6638984
bibtex: '@inproceedings{Abdelaziz_Zeiler_Kolossa_Leutnant_Haeb-Umbach_2013, title={GMM-based
significance decoding}, DOI={10.1109/ICASSP.2013.6638984},
booktitle={Acoustics, Speech and Signal Processing (ICASSP), 2013 IEEE International
Conference on}, author={Abdelaziz, Ahmed H. and Zeiler, Steffen and Kolossa, Dorothea
and Leutnant, Volker and Haeb-Umbach, Reinhold}, year={2013}, pages={6827–6831}
}'
chicago: Abdelaziz, Ahmed H., Steffen Zeiler, Dorothea Kolossa, Volker Leutnant,
and Reinhold Haeb-Umbach. “GMM-Based Significance Decoding.” In Acoustics,
Speech and Signal Processing (ICASSP), 2013 IEEE International Conference On,
6827–31, 2013. https://doi.org/10.1109/ICASSP.2013.6638984.
ieee: A. H. Abdelaziz, S. Zeiler, D. Kolossa, V. Leutnant, and R. Haeb-Umbach, “GMM-based
significance decoding,” in Acoustics, Speech and Signal Processing (ICASSP),
2013 IEEE International Conference on, 2013, pp. 6827–6831.
mla: Abdelaziz, Ahmed H., et al. “GMM-Based Significance Decoding.” Acoustics,
Speech and Signal Processing (ICASSP), 2013 IEEE International Conference On,
2013, pp. 6827–31, doi:10.1109/ICASSP.2013.6638984.
short: 'A.H. Abdelaziz, S. Zeiler, D. Kolossa, V. Leutnant, R. Haeb-Umbach, in:
Acoustics, Speech and Signal Processing (ICASSP), 2013 IEEE International Conference
On, 2013, pp. 6827–6831.'
date_created: 2019-07-12T05:26:53Z
date_updated: 2022-01-06T06:51:07Z
department:
- _id: '54'
doi: 10.1109/ICASSP.2013.6638984
keyword:
- Bayes methods
- Gaussian processes
- convolution
- decision theory
- decoding
- noise
- reverberation
- speech coding
- speech recognition
- Bayesian decision rule
- GMM
- Gaussian mixture models
- additive noise scenarios
- automatic speech recognition systems
- convolutive noise scenarios
- decoding approach
- mathematical framework
- reverberant environments
- significance decoding
- speech feature estimation
- uncertainty-of-observation techniques
- Hidden Markov models
- Maximum likelihood decoding
- Noise
- Speech
- Speech recognition
- Uncertainty
- Uncertainty-of-observation
- modified imputation
- noise robust speech recognition
- significance decoding
- uncertainty decoding
language:
- iso: eng
page: 6827-6831
publication: Acoustics, Speech and Signal Processing (ICASSP), 2013 IEEE International
Conference on
publication_identifier:
issn:
- 1520-6149
status: public
title: GMM-based significance decoding
type: conference
user_id: '44006'
year: '2013'
...
---
_id: '11740'
abstract:
- lang: eng
text: In this contribution we derive the Maximum A-Posteriori (MAP) estimates of
the parameters of a Gaussian Mixture Model (GMM) in the presence of noisy observations.
We assume the distortion to be white Gaussian noise of known mean and variance.
An approximate conjugate prior of the GMM parameters is derived allowing for a
computationally efficient implementation in a sequential estimation framework.
Simulations on artificially generated data demonstrate the superiority of the
proposed method compared to the Maximum Likelihood technique and to the ordinary
MAP approach, whose estimates are corrected by the known statistics of the distortion
in a straightforward manner.
author:
- first_name: Aleksej
full_name: Chinaev, Aleksej
last_name: Chinaev
- first_name: Reinhold
full_name: Haeb-Umbach, Reinhold
id: '242'
last_name: Haeb-Umbach
citation:
ama: 'Chinaev A, Haeb-Umbach R. MAP-based Estimation of the Parameters of a Gaussian
Mixture Model in the Presence of Noisy Observations. In: 38th International
Conference on Acoustics, Speech and Signal Processing (ICASSP 2013). ; 2013:3352-3356.
doi:10.1109/ICASSP.2013.6638279'
apa: Chinaev, A., & Haeb-Umbach, R. (2013). MAP-based Estimation of the Parameters
of a Gaussian Mixture Model in the Presence of Noisy Observations. In 38th
International Conference on Acoustics, Speech and Signal Processing (ICASSP 2013)
(pp. 3352–3356). https://doi.org/10.1109/ICASSP.2013.6638279
bibtex: '@inproceedings{Chinaev_Haeb-Umbach_2013, title={MAP-based Estimation of
the Parameters of a Gaussian Mixture Model in the Presence of Noisy Observations},
DOI={10.1109/ICASSP.2013.6638279},
booktitle={38th International Conference on Acoustics, Speech and Signal Processing
(ICASSP 2013)}, author={Chinaev, Aleksej and Haeb-Umbach, Reinhold}, year={2013},
pages={3352–3356} }'
chicago: Chinaev, Aleksej, and Reinhold Haeb-Umbach. “MAP-Based Estimation of the
Parameters of a Gaussian Mixture Model in the Presence of Noisy Observations.”
In 38th International Conference on Acoustics, Speech and Signal Processing
(ICASSP 2013), 3352–56, 2013. https://doi.org/10.1109/ICASSP.2013.6638279.
ieee: A. Chinaev and R. Haeb-Umbach, “MAP-based Estimation of the Parameters of
a Gaussian Mixture Model in the Presence of Noisy Observations,” in 38th International
Conference on Acoustics, Speech and Signal Processing (ICASSP 2013), 2013,
pp. 3352–3356.
mla: Chinaev, Aleksej, and Reinhold Haeb-Umbach. “MAP-Based Estimation of the Parameters
of a Gaussian Mixture Model in the Presence of Noisy Observations.” 38th International
Conference on Acoustics, Speech and Signal Processing (ICASSP 2013), 2013,
pp. 3352–56, doi:10.1109/ICASSP.2013.6638279.
short: 'A. Chinaev, R. Haeb-Umbach, in: 38th International Conference on Acoustics,
Speech and Signal Processing (ICASSP 2013), 2013, pp. 3352–3356.'
date_created: 2019-07-12T05:27:20Z
date_updated: 2022-01-06T06:51:08Z
department:
- _id: '54'
doi: 10.1109/ICASSP.2013.6638279
keyword:
- Gaussian noise
- maximum likelihood estimation
- parameter estimation
- GMM parameter
- Gaussian mixture model
- MAP estimation
- Map-based estimation
- maximum a-posteriori estimation
- maximum likelihood technique
- noisy observation
- sequential estimation framework
- white Gaussian noise
- Additive noise
- Gaussian mixture model
- Maximum likelihood estimation
- Noise measurement
- Gaussian mixture model
- Maximum a posteriori estimation
- Maximum likelihood estimation
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://groups.uni-paderborn.de/nt/pubs/2013/ChHa13.pdf
oa: '1'
page: 3352-3356
publication: 38th International Conference on Acoustics, Speech and Signal Processing
(ICASSP 2013)
publication_identifier:
issn:
- 1520-6149
related_material:
link:
- description: Poster
relation: supplementary_material
url: https://groups.uni-paderborn.de/nt/pubs/2013/ChHa13_Poster.pdf
status: public
title: MAP-based Estimation of the Parameters of a Gaussian Mixture Model in the Presence
of Noisy Observations
type: conference
user_id: '44006'
year: '2013'
...
---
_id: '11742'
abstract:
- lang: eng
text: In this paper we present an improved version of the recently proposed Maximum
A-Posteriori (MAP) based noise power spectral density estimator. An empirical
bias compensation and bandwidth adjustment reduce bias and variance of the noise
variance estimates. The main advantage of the MAP-based postprocessor is its low
estimation variance. The estimator is employed in the second stage of a two-stage
single-channel speech enhancement system, where eight different state-of-the-art
noise tracking algorithms were tested in the first stage. While the postprocessor
hardly affects the results in stationary noise scenarios, it becomes the more
effective the more nonstationary the noise is. The proposed postprocessor was
able to improve all systems in babble noise w.r.t. the perceptual evaluation of
speech quality performance.
author:
- first_name: Aleksej
full_name: Chinaev, Aleksej
last_name: Chinaev
- first_name: Reinhold
full_name: Haeb-Umbach, Reinhold
id: '242'
last_name: Haeb-Umbach
- first_name: Jalal
full_name: Taghia, Jalal
last_name: Taghia
- first_name: Rainer
full_name: Martin, Rainer
last_name: Martin
citation:
ama: 'Chinaev A, Haeb-Umbach R, Taghia J, Martin R. Improved Single-Channel Nonstationary
Noise Tracking by an Optimized MAP-based Postprocessor. In: 38th International
Conference on Acoustics, Speech and Signal Processing (ICASSP 2013). ; 2013:7477-7481.
doi:10.1109/ICASSP.2013.6639116'
apa: Chinaev, A., Haeb-Umbach, R., Taghia, J., & Martin, R. (2013). Improved
Single-Channel Nonstationary Noise Tracking by an Optimized MAP-based Postprocessor.
In 38th International Conference on Acoustics, Speech and Signal Processing
(ICASSP 2013) (pp. 7477–7481). https://doi.org/10.1109/ICASSP.2013.6639116
bibtex: '@inproceedings{Chinaev_Haeb-Umbach_Taghia_Martin_2013, title={Improved
Single-Channel Nonstationary Noise Tracking by an Optimized MAP-based Postprocessor},
DOI={10.1109/ICASSP.2013.6639116},
booktitle={38th International Conference on Acoustics, Speech and Signal Processing
(ICASSP 2013)}, author={Chinaev, Aleksej and Haeb-Umbach, Reinhold and Taghia,
Jalal and Martin, Rainer}, year={2013}, pages={7477–7481} }'
chicago: Chinaev, Aleksej, Reinhold Haeb-Umbach, Jalal Taghia, and Rainer Martin.
“Improved Single-Channel Nonstationary Noise Tracking by an Optimized MAP-Based
Postprocessor.” In 38th International Conference on Acoustics, Speech and Signal
Processing (ICASSP 2013), 7477–81, 2013. https://doi.org/10.1109/ICASSP.2013.6639116.
ieee: A. Chinaev, R. Haeb-Umbach, J. Taghia, and R. Martin, “Improved Single-Channel
Nonstationary Noise Tracking by an Optimized MAP-based Postprocessor,” in 38th
International Conference on Acoustics, Speech and Signal Processing (ICASSP 2013),
2013, pp. 7477–7481.
mla: Chinaev, Aleksej, et al. “Improved Single-Channel Nonstationary Noise Tracking
by an Optimized MAP-Based Postprocessor.” 38th International Conference on
Acoustics, Speech and Signal Processing (ICASSP 2013), 2013, pp. 7477–81,
doi:10.1109/ICASSP.2013.6639116.
short: 'A. Chinaev, R. Haeb-Umbach, J. Taghia, R. Martin, in: 38th International
Conference on Acoustics, Speech and Signal Processing (ICASSP 2013), 2013, pp.
7477–7481.'
date_created: 2019-07-12T05:27:23Z
date_updated: 2022-01-06T06:51:08Z
department:
- _id: '54'
doi: 10.1109/ICASSP.2013.6639116
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://groups.uni-paderborn.de/nt/pubs/2013/ChHaTaRa13.pdf
oa: '1'
page: 7477-7481
publication: 38th International Conference on Acoustics, Speech and Signal Processing
(ICASSP 2013)
publication_identifier:
issn:
- 1520-6149
related_material:
link:
- description: Poster
relation: supplementary_material
url: https://groups.uni-paderborn.de/nt/pubs/2013/ChHaTaRa13_Poster.pdf
status: public
title: Improved Single-Channel Nonstationary Noise Tracking by an Optimized MAP-based
Postprocessor
type: conference
user_id: '44006'
year: '2013'
...
---
_id: '11762'
abstract:
- lang: eng
text: Among the different configurations of multi-microphone systems, e.g., in applications
of speech dereverberation or denoising, we consider the case without a priori
information of the microphone-array geometry. This naturally invokes explicit
or implicit identification of source-receiver transfer functions as an indirect
description of the microphone-array configuration. However, this blind channel
identification (BCI) has been difficult due to the lack of unique identifiability
in the presence of observation noise or near-common channel zeros. In this paper,
we study the implicit BCI performance of blind signal enhancement techniques such
as the adaptive principal component analysis (PCA) or the iterative blind equalization
and channel identification (BENCH). To this end, we make use of a recently proposed
metric, the normalized filter-projection misalignment (NFPM), which is tailored
for BCI evaluation in ill-conditioned (e.g., noisy) scenarios. The resulting understanding
of implicit BCI performance can help to judge the behavior of multi-microphone
speech enhancement systems and the suitability of implicit BCI to serve channel-based
(i.e., channel-informed) enhancement.
author:
- first_name: Gerald
full_name: Enzner, Gerald
last_name: Enzner
- first_name: Dominic
full_name: Schmid, Dominic
last_name: Schmid
- first_name: Reinhold
full_name: Haeb-Umbach, Reinhold
id: '242'
last_name: Haeb-Umbach
citation:
ama: 'Enzner G, Schmid D, Haeb-Umbach R. On the Acoustic Channel Identification
in Multi-Microphone Systems via Adaptive Blind Signal Enhancement Techniques.
In: 21th European Signal Processing Conference (EUSIPCO 2013). ; 2013.'
apa: Enzner, G., Schmid, D., & Haeb-Umbach, R. (2013). On the Acoustic Channel
Identification in Multi-Microphone Systems via Adaptive Blind Signal Enhancement
Techniques. In 21th European Signal Processing Conference (EUSIPCO 2013).
bibtex: '@inproceedings{Enzner_Schmid_Haeb-Umbach_2013, title={On the Acoustic Channel
Identification in Multi-Microphone Systems via Adaptive Blind Signal Enhancement
Techniques}, booktitle={21th European Signal Processing Conference (EUSIPCO 2013)},
author={Enzner, Gerald and Schmid, Dominic and Haeb-Umbach, Reinhold}, year={2013}
}'
chicago: Enzner, Gerald, Dominic Schmid, and Reinhold Haeb-Umbach. “On the Acoustic
Channel Identification in Multi-Microphone Systems via Adaptive Blind Signal Enhancement
Techniques.” In 21th European Signal Processing Conference (EUSIPCO 2013),
2013.
ieee: G. Enzner, D. Schmid, and R. Haeb-Umbach, “On the Acoustic Channel Identification
in Multi-Microphone Systems via Adaptive Blind Signal Enhancement Techniques,”
in 21th European Signal Processing Conference (EUSIPCO 2013), 2013.
mla: Enzner, Gerald, et al. “On the Acoustic Channel Identification in Multi-Microphone
Systems via Adaptive Blind Signal Enhancement Techniques.” 21th European Signal
Processing Conference (EUSIPCO 2013), 2013.
short: 'G. Enzner, D. Schmid, R. Haeb-Umbach, in: 21th European Signal Processing
Conference (EUSIPCO 2013), 2013.'
date_created: 2019-07-12T05:27:46Z
date_updated: 2022-01-06T06:51:08Z
department:
- _id: '54'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://groups.uni-paderborn.de/nt/pubs/2013/EnScHa2013.pdf
oa: '1'
publication: 21th European Signal Processing Conference (EUSIPCO 2013)
status: public
title: On the Acoustic Channel Identification in Multi-Microphone Systems via Adaptive
Blind Signal Enhancement Techniques
type: conference
user_id: '44006'
year: '2013'
...
---
_id: '11815'
author:
- first_name: Jahn
full_name: Heymann, Jahn
id: '9168'
last_name: Heymann
- first_name: Oliver
full_name: Walter, Oliver
last_name: Walter
- first_name: Reinhold
full_name: Haeb-Umbach, Reinhold
id: '242'
last_name: Haeb-Umbach
- first_name: Bhiksha
full_name: Raj, Bhiksha
last_name: Raj
citation:
ama: 'Heymann J, Walter O, Haeb-Umbach R, Raj B. Unsupervised Word Segmentation
from Noisy Input. In: Automatic Speech Recognition and Understanding Workshop
(ASRU 2013). ; 2013.'
apa: Heymann, J., Walter, O., Haeb-Umbach, R., & Raj, B. (2013). Unsupervised
Word Segmentation from Noisy Input. In Automatic Speech Recognition and Understanding
Workshop (ASRU 2013).
bibtex: '@inproceedings{Heymann_Walter_Haeb-Umbach_Raj_2013, title={Unsupervised
Word Segmentation from Noisy Input}, booktitle={Automatic Speech Recognition and
Understanding Workshop (ASRU 2013)}, author={Heymann, Jahn and Walter, Oliver
and Haeb-Umbach, Reinhold and Raj, Bhiksha}, year={2013} }'
chicago: Heymann, Jahn, Oliver Walter, Reinhold Haeb-Umbach, and Bhiksha Raj. “Unsupervised
Word Segmentation from Noisy Input.” In Automatic Speech Recognition and Understanding
Workshop (ASRU 2013), 2013.
ieee: J. Heymann, O. Walter, R. Haeb-Umbach, and B. Raj, “Unsupervised Word Segmentation
from Noisy Input,” in Automatic Speech Recognition and Understanding Workshop
(ASRU 2013), 2013.
mla: Heymann, Jahn, et al. “Unsupervised Word Segmentation from Noisy Input.” Automatic
Speech Recognition and Understanding Workshop (ASRU 2013), 2013.
short: 'J. Heymann, O. Walter, R. Haeb-Umbach, B. Raj, in: Automatic Speech Recognition
and Understanding Workshop (ASRU 2013), 2013.'
date_created: 2019-07-12T05:28:47Z
date_updated: 2022-01-06T06:51:09Z
department:
- _id: '54'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://groups.uni-paderborn.de/nt/pubs/2013/HeWaHaRa13.pdf
oa: '1'
publication: Automatic Speech Recognition and Understanding Workshop (ASRU 2013)
related_material:
link:
- description: Poster
relation: supplementary_material
url: https://groups.uni-paderborn.de/nt/pubs/2013/HeWaHaRa_Poster.pdf
status: public
title: Unsupervised Word Segmentation from Noisy Input
type: conference
user_id: '44006'
year: '2013'
...
---
_id: '11816'
abstract:
- lang: eng
text: In this paper, we consider the Maximum Likelihood (ML) estimation of the parameters
of a GAUSSIAN in the presence of censored, i.e., clipped data. We show that the
resulting Expectation Maximization (EM) algorithm delivers virtually biasfree
and efficient estimates, and we discuss its convergence properties. We also discuss
optimal classification in the presence of censored data. Censored data are frequently
encountered in wireless LAN positioning systems based on the fingerprinting method
employing signal strength measurements, due to the limited sensitivity of the
portable devices. Experiments both on simulated and real-world data demonstrate
the effectiveness of the proposed algorithms.
author:
- first_name: Manh Kha
full_name: Hoang, Manh Kha
last_name: Hoang
- first_name: Reinhold
full_name: Haeb-Umbach, Reinhold
id: '242'
last_name: Haeb-Umbach
citation:
ama: 'Hoang MK, Haeb-Umbach R. Parameter estimation and classification of censored
Gaussian data with application to WiFi indoor positioning. In: 38th International
Conference on Acoustics, Speech, and Signal Processing (ICASSP 2013). ; 2013:3721-3725.
doi:10.1109/ICASSP.2013.6638353'
apa: Hoang, M. K., & Haeb-Umbach, R. (2013). Parameter estimation and classification
of censored Gaussian data with application to WiFi indoor positioning. In 38th
International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2013)
(pp. 3721–3725). https://doi.org/10.1109/ICASSP.2013.6638353
bibtex: '@inproceedings{Hoang_Haeb-Umbach_2013, title={Parameter estimation and
classification of censored Gaussian data with application to WiFi indoor positioning},
DOI={10.1109/ICASSP.2013.6638353},
booktitle={38th International Conference on Acoustics, Speech, and Signal Processing
(ICASSP 2013)}, author={Hoang, Manh Kha and Haeb-Umbach, Reinhold}, year={2013},
pages={3721–3725} }'
chicago: Hoang, Manh Kha, and Reinhold Haeb-Umbach. “Parameter Estimation and Classification
of Censored Gaussian Data with Application to WiFi Indoor Positioning.” In 38th
International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2013),
3721–25, 2013. https://doi.org/10.1109/ICASSP.2013.6638353.
ieee: M. K. Hoang and R. Haeb-Umbach, “Parameter estimation and classification of
censored Gaussian data with application to WiFi indoor positioning,” in 38th
International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2013),
2013, pp. 3721–3725.
mla: Hoang, Manh Kha, and Reinhold Haeb-Umbach. “Parameter Estimation and Classification
of Censored Gaussian Data with Application to WiFi Indoor Positioning.” 38th
International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2013),
2013, pp. 3721–25, doi:10.1109/ICASSP.2013.6638353.
short: 'M.K. Hoang, R. Haeb-Umbach, in: 38th International Conference on Acoustics,
Speech, and Signal Processing (ICASSP 2013), 2013, pp. 3721–3725.'
date_created: 2019-07-12T05:28:48Z
date_updated: 2022-01-06T06:51:09Z
department:
- _id: '54'
doi: 10.1109/ICASSP.2013.6638353
keyword:
- Gaussian processes
- Global Positioning System
- convergence
- expectation-maximisation algorithm
- fingerprint identification
- indoor radio
- signal classification
- wireless LAN
- EM algorithm
- ML estimation
- WiFi indoor positioning
- censored Gaussian data classification
- clipped data
- convergence properties
- expectation maximization algorithm
- fingerprinting method
- maximum likelihood estimation
- optimal classification
- parameters estimation
- portable devices sensitivity
- signal strength measurements
- wireless LAN positioning systems
- Convergence
- IEEE 802.11 Standards
- Maximum likelihood estimation
- Parameter estimation
- Position measurement
- Training
- Indoor positioning
- censored data
- expectation maximization
- signal strength
- wireless LAN
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://groups.uni-paderborn.de/nt/pubs/2013/HoHa2013.pdf
oa: '1'
page: 3721-3725
publication: 38th International Conference on Acoustics, Speech, and Signal Processing
(ICASSP 2013)
publication_identifier:
issn:
- 1520-6149
related_material:
link:
- description: Poster
relation: supplementary_material
url: https://groups.uni-paderborn.de/nt/pubs/2013/HoHa2013_Poster.pdf
status: public
title: Parameter estimation and classification of censored Gaussian data with application
to WiFi indoor positioning
type: conference
user_id: '44006'
year: '2013'
...