---
_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'
...
---
_id: '11841'
abstract:
- lang: eng
text: Recently, substantial progress has been made in the field of reverberant speech
signal processing, including both single- and multichannel de-reverberation techniques,
and automatic speech recognition (ASR) techniques robust to reverberation. To
evaluate state-of-the-art algorithms and obtain new insights regarding potential
future research directions, we propose a common evaluation framework including
datasets, tasks, and evaluation metrics for both speech enhancement and ASR techniques.
The proposed framework will be used as a common basis for the REVERB (REverberant
Voice Enhancement and Recognition Benchmark) challenge. This paper describes the
rationale behind the challenge, and provides a detailed description of the evaluation
framework and benchmark results.
author:
- first_name: Keisuke
full_name: Kinoshita, Keisuke
last_name: Kinoshita
- first_name: Marc
full_name: Delcroix, Marc
last_name: Delcroix
- first_name: Takuya
full_name: Yoshioka, Takuya
last_name: Yoshioka
- first_name: Tomohiro
full_name: Nakatani, Tomohiro
last_name: Nakatani
- first_name: Emanuel
full_name: Habets, Emanuel
last_name: Habets
- first_name: Reinhold
full_name: Haeb-Umbach, Reinhold
id: '242'
last_name: Haeb-Umbach
- first_name: Volker
full_name: Leutnant, Volker
last_name: Leutnant
- first_name: Armin
full_name: Sehr, Armin
last_name: Sehr
- first_name: Walter
full_name: Kellermann, Walter
last_name: Kellermann
- first_name: Roland
full_name: Maas, Roland
last_name: Maas
- first_name: Sharon
full_name: Gannot, Sharon
last_name: Gannot
- first_name: Bhiksha
full_name: Raj, Bhiksha
last_name: Raj
citation:
ama: 'Kinoshita K, Delcroix M, Yoshioka T, et al. The reverb challenge: a common
evaluation framework for dereverberation and recognition of reverberant speech.
In: IEEE Workshop on Applications of Signal Processing to Audio and Acoustics
. ; 2013:22-23.'
apa: 'Kinoshita, K., Delcroix, M., Yoshioka, T., Nakatani, T., Habets, E., Haeb-Umbach,
R., … Raj, B. (2013). The reverb challenge: a common evaluation framework for
dereverberation and recognition of reverberant speech. In IEEE Workshop on
Applications of Signal Processing to Audio and Acoustics (pp. 22–23).'
bibtex: '@inproceedings{Kinoshita_Delcroix_Yoshioka_Nakatani_Habets_Haeb-Umbach_Leutnant_Sehr_Kellermann_Maas_et
al._2013, title={The reverb challenge: a common evaluation framework for dereverberation
and recognition of reverberant speech}, booktitle={ IEEE Workshop on Applications
of Signal Processing to Audio and Acoustics }, author={Kinoshita, Keisuke and
Delcroix, Marc and Yoshioka, Takuya and Nakatani, Tomohiro and Habets, Emanuel
and Haeb-Umbach, Reinhold and Leutnant, Volker and Sehr, Armin and Kellermann,
Walter and Maas, Roland and et al.}, year={2013}, pages={22–23} }'
chicago: 'Kinoshita, Keisuke, Marc Delcroix, Takuya Yoshioka, Tomohiro Nakatani,
Emanuel Habets, Reinhold Haeb-Umbach, Volker Leutnant, et al. “The Reverb Challenge:
A Common Evaluation Framework for Dereverberation and Recognition of Reverberant
Speech.” In IEEE Workshop on Applications of Signal Processing to Audio and
Acoustics , 22–23, 2013.'
ieee: 'K. Kinoshita et al., “The reverb challenge: a common evaluation framework
for dereverberation and recognition of reverberant speech,” in IEEE Workshop
on Applications of Signal Processing to Audio and Acoustics , 2013, pp. 22–23.'
mla: 'Kinoshita, Keisuke, et al. “The Reverb Challenge: A Common Evaluation Framework
for Dereverberation and Recognition of Reverberant Speech.” IEEE Workshop
on Applications of Signal Processing to Audio and Acoustics , 2013, pp. 22–23.'
short: 'K. Kinoshita, M. Delcroix, T. Yoshioka, T. Nakatani, E. Habets, R. Haeb-Umbach,
V. Leutnant, A. Sehr, W. Kellermann, R. Maas, S. Gannot, B. Raj, in: IEEE Workshop
on Applications of Signal Processing to Audio and Acoustics , 2013, pp. 22–23.'
date_created: 2019-07-12T05:29:17Z
date_updated: 2022-01-06T06:51:11Z
department:
- _id: '54'
keyword:
- Reverberant speech
- dereverberation
- ASR
- evaluation
- challenge
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://groups.uni-paderborn.de/nt/pubs/2013/Reverb2013.pdf
oa: '1'
page: ' 22-23 '
publication: ' IEEE Workshop on Applications of Signal Processing to Audio and Acoustics '
status: public
title: 'The reverb challenge: a common evaluation framework for dereverberation and
recognition of reverberant speech'
type: conference
user_id: '44006'
year: '2013'
...
---
_id: '11862'
abstract:
- lang: eng
text: In this contribution we extend a previously proposed Bayesian approach for
the enhancement of reverberant logarithmic mel power spectral coefficients for
robust automatic speech recognition to the additional compensation of background
noise. A recently proposed observation model is employed whose time-variant observation
error statistics are obtained as a side product of the inference of the a posteriori
probability density function of the clean speech feature vectors. Further a reduction
of the computational effort and the memory requirements are achieved by using
a recursive formulation of the observation model. The performance of the proposed
algorithms is first experimentally studied on a connected digits recognition task
with artificially created noisy reverberant data. It is shown that the use of
the time-variant observation error model leads to a significant error rate reduction
at low signal-to-noise ratios compared to a time-invariant model. Further experiments
were conducted on a 5000 word task recorded in a reverberant and noisy environment.
A significant word error rate reduction was obtained demonstrating the effectiveness
of the approach on real-world 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. Bayesian Feature Enhancement for Reverberation
and Noise Robust Speech Recognition. IEEE Transactions on Audio, Speech, and
Language Processing. 2013;21(8):1640-1652. doi:10.1109/TASL.2013.2258013
apa: Leutnant, V., Krueger, A., & Haeb-Umbach, R. (2013). Bayesian Feature Enhancement
for Reverberation and Noise Robust Speech Recognition. IEEE Transactions on
Audio, Speech, and Language Processing, 21(8), 1640–1652. https://doi.org/10.1109/TASL.2013.2258013
bibtex: '@article{Leutnant_Krueger_Haeb-Umbach_2013, title={Bayesian Feature Enhancement
for Reverberation and Noise Robust Speech Recognition}, volume={21}, DOI={10.1109/TASL.2013.2258013},
number={8}, journal={IEEE Transactions on Audio, Speech, and Language Processing},
author={Leutnant, Volker and Krueger, Alexander and Haeb-Umbach, Reinhold}, year={2013},
pages={1640–1652} }'
chicago: 'Leutnant, Volker, Alexander Krueger, and Reinhold Haeb-Umbach. “Bayesian
Feature Enhancement for Reverberation and Noise Robust Speech Recognition.” IEEE
Transactions on Audio, Speech, and Language Processing 21, no. 8 (2013): 1640–52.
https://doi.org/10.1109/TASL.2013.2258013.'
ieee: V. Leutnant, A. Krueger, and R. Haeb-Umbach, “Bayesian Feature Enhancement
for Reverberation and Noise Robust Speech Recognition,” IEEE Transactions on
Audio, Speech, and Language Processing, vol. 21, no. 8, pp. 1640–1652, 2013.
mla: Leutnant, Volker, et al. “Bayesian Feature Enhancement for Reverberation and
Noise Robust Speech Recognition.” IEEE Transactions on Audio, Speech, and Language
Processing, vol. 21, no. 8, 2013, pp. 1640–52, doi:10.1109/TASL.2013.2258013.
short: V. Leutnant, A. Krueger, R. Haeb-Umbach, IEEE Transactions on Audio, Speech,
and Language Processing 21 (2013) 1640–1652.
date_created: 2019-07-12T05:29:42Z
date_updated: 2022-01-06T06:51:11Z
department:
- _id: '54'
doi: 10.1109/TASL.2013.2258013
intvolume: ' 21'
issue: '8'
keyword:
- Bayes methods
- compensation
- error statistics
- reverberation
- speech recognition
- Bayesian feature enhancement
- background noise
- clean speech feature vectors
- compensation
- connected digits recognition task
- error statistics
- memory requirements
- noisy reverberant data
- posteriori probability density function
- recursive formulation
- reverberant logarithmic mel power spectral coefficients
- robust automatic speech recognition
- signal-to-noise ratios
- time-variant observation
- word error rate reduction
- Robust automatic speech recognition
- model-based Bayesian feature enhancement
- observation model for reverberant and noisy speech
- recursive observation model
language:
- iso: eng
page: 1640-1652
publication: IEEE Transactions on Audio, Speech, and Language Processing
status: public
title: Bayesian Feature Enhancement for Reverberation and Noise Robust Speech Recognition
type: journal_article
user_id: '44006'
volume: 21
year: '2013'
...
---
_id: '11909'
abstract:
- lang: eng
text: 'We present a novel method to exploit correlations of adjacent time-frequency
(TF)-slots for a sparseness-based blind speech separation (BSS) system. Usually,
these correlations are exploited by some heuristic smoothing techniques in the
post-processing of the estimated soft TF masks. We propose a different approach:
Based on our previous work with one-dimensional (1D)-hidden Markov models (HMMs)
along the time axis we extend the modeling to two-dimensional (2D)-HMMs to exploit
both temporal and spectral correlations in the speech signal. Based on the principles
of turbo decoding we solved the complex inference of 2D-HMMs by a modified forward-backward
algorithm which operates alternatingly along the time and the frequency axis.
Extrinsic information is exchanged between these steps such that increasingly
better soft time-frequency masks are obtained, leading to improved speech separation
performance in highly reverberant recording conditions.'
author:
- 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: 'Tran Vu DH, Haeb-Umbach R. Blind Speech Separation Exploiting Temporal and
Spectral Correlations Using Turbo Decoding of 2D-HMMs. In: 21th European Signal
Processing Conference (EUSIPCO 2013). ; 2013.'
apa: Tran Vu, D. H., & Haeb-Umbach, R. (2013). Blind Speech Separation Exploiting
Temporal and Spectral Correlations Using Turbo Decoding of 2D-HMMs. In 21th
European Signal Processing Conference (EUSIPCO 2013).
bibtex: '@inproceedings{Tran Vu_Haeb-Umbach_2013, title={Blind Speech Separation
Exploiting Temporal and Spectral Correlations Using Turbo Decoding of 2D-HMMs},
booktitle={21th European Signal Processing Conference (EUSIPCO 2013)}, author={Tran
Vu, Dang Hai and Haeb-Umbach, Reinhold}, year={2013} }'
chicago: Tran Vu, Dang Hai, and Reinhold Haeb-Umbach. “Blind Speech Separation Exploiting
Temporal and Spectral Correlations Using Turbo Decoding of 2D-HMMs.” In 21th
European Signal Processing Conference (EUSIPCO 2013), 2013.
ieee: D. H. Tran Vu and R. Haeb-Umbach, “Blind Speech Separation Exploiting Temporal
and Spectral Correlations Using Turbo Decoding of 2D-HMMs,” in 21th European
Signal Processing Conference (EUSIPCO 2013), 2013.
mla: Tran Vu, Dang Hai, and Reinhold Haeb-Umbach. “Blind Speech Separation Exploiting
Temporal and Spectral Correlations Using Turbo Decoding of 2D-HMMs.” 21th European
Signal Processing Conference (EUSIPCO 2013), 2013.
short: 'D.H. Tran Vu, R. Haeb-Umbach, in: 21th European Signal Processing Conference
(EUSIPCO 2013), 2013.'
date_created: 2019-07-12T05:30:36Z
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/2013/TrHa2013_01.pdf
oa: '1'
publication: 21th European Signal Processing Conference (EUSIPCO 2013)
related_material:
link:
- description: Presentation
relation: supplementary_material
url: https://groups.uni-paderborn.de/nt/pubs/2013/TrHa2013_01_Presentation.pdf
status: public
title: Blind Speech Separation Exploiting Temporal and Spectral Correlations Using
Turbo Decoding of 2D-HMMs
type: conference
user_id: '44006'
year: '2013'
...
---
_id: '11917'
abstract:
- lang: eng
text: In this paper we present a speech presence probability (SPP) estimation algorithmwhich
exploits both temporal and spectral correlations of speech. To this end, the SPP
estimation is formulated as the posterior probability estimation of the states
of a two-dimensional (2D) Hidden Markov Model (HMM). We derive an iterative algorithm
to decode the 2D-HMM which is based on the turbo principle. The experimental results
show that indeed the SPP estimates improve from iteration to iteration, and further
clearly outperform another state-of-the-art SPP estimation algorithm.
author:
- first_name: Dang Hai Tran
full_name: Vu, Dang Hai Tran
last_name: Vu
- first_name: Reinhold
full_name: Haeb-Umbach, Reinhold
id: '242'
last_name: Haeb-Umbach
citation:
ama: 'Vu DHT, Haeb-Umbach R. Using the turbo principle for exploiting temporal and
spectral correlations in speech presence probability estimation. In: 38th International
Conference on Acoustics, Speech and Signal Processing (ICASSP 2013). ; 2013:863-867.
doi:10.1109/ICASSP.2013.6637771'
apa: Vu, D. H. T., & Haeb-Umbach, R. (2013). Using the turbo principle for exploiting
temporal and spectral correlations in speech presence probability estimation.
In 38th International Conference on Acoustics, Speech and Signal Processing
(ICASSP 2013) (pp. 863–867). https://doi.org/10.1109/ICASSP.2013.6637771
bibtex: '@inproceedings{Vu_Haeb-Umbach_2013, title={Using the turbo principle for
exploiting temporal and spectral correlations in speech presence probability estimation},
DOI={10.1109/ICASSP.2013.6637771},
booktitle={38th International Conference on Acoustics, Speech and Signal Processing
(ICASSP 2013)}, author={Vu, Dang Hai Tran and Haeb-Umbach, Reinhold}, year={2013},
pages={863–867} }'
chicago: Vu, Dang Hai Tran, and Reinhold Haeb-Umbach. “Using the Turbo Principle
for Exploiting Temporal and Spectral Correlations in Speech Presence Probability
Estimation.” In 38th International Conference on Acoustics, Speech and Signal
Processing (ICASSP 2013), 863–67, 2013. https://doi.org/10.1109/ICASSP.2013.6637771.
ieee: D. H. T. Vu and R. Haeb-Umbach, “Using the turbo principle for exploiting
temporal and spectral correlations in speech presence probability estimation,”
in 38th International Conference on Acoustics, Speech and Signal Processing
(ICASSP 2013), 2013, pp. 863–867.
mla: Vu, Dang Hai Tran, and Reinhold Haeb-Umbach. “Using the Turbo Principle for
Exploiting Temporal and Spectral Correlations in Speech Presence Probability Estimation.”
38th International Conference on Acoustics, Speech and Signal Processing (ICASSP
2013), 2013, pp. 863–67, doi:10.1109/ICASSP.2013.6637771.
short: 'D.H.T. Vu, R. Haeb-Umbach, in: 38th International Conference on Acoustics,
Speech and Signal Processing (ICASSP 2013), 2013, pp. 863–867.'
date_created: 2019-07-12T05:30:45Z
date_updated: 2022-01-06T06:51:12Z
department:
- _id: '54'
doi: 10.1109/ICASSP.2013.6637771
keyword:
- correlation methods
- estimation theory
- hidden Markov models
- iterative methods
- probability
- spectral analysis
- speech processing
- 2D HMM
- SPP estimates
- iterative algorithm
- posterior probability estimation
- spectral correlation
- speech presence probability estimation
- state-of-the-art SPP estimation algorithm
- temporal correlation
- turbo principle
- two-dimensional hidden Markov model
- Correlation
- Decoding
- Estimation
- Iterative decoding
- Noise
- Speech
- Vectors
language:
- iso: eng
page: 863-867
publication: 38th International Conference on Acoustics, Speech and Signal Processing
(ICASSP 2013)
publication_identifier:
issn:
- 1520-6149
status: public
title: Using the turbo principle for exploiting temporal and spectral correlations
in speech presence probability estimation
type: conference
user_id: '44006'
year: '2013'
...
---
_id: '11921'
abstract:
- lang: eng
text: In this paper we consider the unsupervised word discovery from phonetic input.
We employ a word segmentation algorithm which simultaneously develops a lexicon,
i.e., the transcription of a word in terms of a phone sequence, learns a n-gram
language model describing word and word sequence probabilities, and carries out
the segmentation itself. The underlying statistical model is that of a Pitman-Yor
process, a concept known from Bayesian non-parametrics, which allows for an a
priori unknown and unlimited number of different words. Using a hierarchy of Pitman-Yor
processes, language models of different order can be employed and nesting it with
another hierarchy of Pitman-Yor processes on the phone level allows for backing
off unknown word unigrams by phone m-grams. We present results on a large-vocabulary
task, assuming an error-free phone sequence is given. We finish by discussing
options how to cope with noisy phone sequences.
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: Sourish
full_name: Chaudhuri, Sourish
last_name: Chaudhuri
- first_name: Bhiksha
full_name: Raj, Bhiksha
last_name: Raj
citation:
ama: 'Walter O, Haeb-Umbach R, Chaudhuri S, Raj B. Unsupervised Word Discovery from
Phonetic Input Using Nested Pitman-Yor Language Modeling. In: IEEE International
Conference on Robotics and Automation (ICRA 2013). ; 2013.'
apa: Walter, O., Haeb-Umbach, R., Chaudhuri, S., & Raj, B. (2013). Unsupervised
Word Discovery from Phonetic Input Using Nested Pitman-Yor Language Modeling.
In IEEE International Conference on Robotics and Automation (ICRA 2013).
bibtex: '@inproceedings{Walter_Haeb-Umbach_Chaudhuri_Raj_2013, title={Unsupervised
Word Discovery from Phonetic Input Using Nested Pitman-Yor Language Modeling},
booktitle={IEEE International Conference on Robotics and Automation (ICRA 2013)},
author={Walter, Oliver and Haeb-Umbach, Reinhold and Chaudhuri, Sourish and Raj,
Bhiksha}, year={2013} }'
chicago: Walter, Oliver, Reinhold Haeb-Umbach, Sourish Chaudhuri, and Bhiksha Raj.
“Unsupervised Word Discovery from Phonetic Input Using Nested Pitman-Yor Language
Modeling.” In IEEE International Conference on Robotics and Automation (ICRA
2013), 2013.
ieee: O. Walter, R. Haeb-Umbach, S. Chaudhuri, and B. Raj, “Unsupervised Word Discovery
from Phonetic Input Using Nested Pitman-Yor Language Modeling,” in IEEE International
Conference on Robotics and Automation (ICRA 2013), 2013.
mla: Walter, Oliver, et al. “Unsupervised Word Discovery from Phonetic Input Using
Nested Pitman-Yor Language Modeling.” IEEE International Conference on Robotics
and Automation (ICRA 2013), 2013.
short: 'O. Walter, R. Haeb-Umbach, S. Chaudhuri, B. Raj, in: IEEE International
Conference on Robotics and Automation (ICRA 2013), 2013.'
date_created: 2019-07-12T05:30:50Z
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/2013/WaHaChRa2013.pdf
oa: '1'
publication: IEEE International Conference on Robotics and Automation (ICRA 2013)
related_material:
link:
- description: Poster
relation: supplementary_material
url: https://groups.uni-paderborn.de/nt/pubs/2013/WaHaChRa2013_Poster.pdf
- description: Spotlight
relation: supplementary_material
url: https://groups.uni-paderborn.de/nt/pubs/2013/WaHaChRa2013_Spotlight.pdf
status: public
title: Unsupervised Word Discovery from Phonetic Input Using Nested Pitman-Yor Language
Modeling
type: conference
user_id: '44006'
year: '2013'
...
---
_id: '11924'
author:
- first_name: Oliver
full_name: Walter, Oliver
last_name: Walter
- first_name: Timo
full_name: Korthals, Timo
last_name: Korthals
- 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: 'Walter O, Korthals T, Haeb-Umbach R, Raj B. Hierarchical System for Word Discovery
Exploiting DTW-Based Initialization. In: Automatic Speech Recognition and Understanding
Workshop (ASRU 2013). ; 2013.'
apa: Walter, O., Korthals, T., Haeb-Umbach, R., & Raj, B. (2013). Hierarchical
System for Word Discovery Exploiting DTW-Based Initialization. In Automatic
Speech Recognition and Understanding Workshop (ASRU 2013).
bibtex: '@inproceedings{Walter_Korthals_Haeb-Umbach_Raj_2013, title={Hierarchical
System for Word Discovery Exploiting DTW-Based Initialization}, booktitle={Automatic
Speech Recognition and Understanding Workshop (ASRU 2013)}, author={Walter, Oliver
and Korthals, Timo and Haeb-Umbach, Reinhold and Raj, Bhiksha}, year={2013} }'
chicago: Walter, Oliver, Timo Korthals, Reinhold Haeb-Umbach, and Bhiksha Raj. “Hierarchical
System for Word Discovery Exploiting DTW-Based Initialization.” In Automatic
Speech Recognition and Understanding Workshop (ASRU 2013), 2013.
ieee: O. Walter, T. Korthals, R. Haeb-Umbach, and B. Raj, “Hierarchical System for
Word Discovery Exploiting DTW-Based Initialization,” in Automatic Speech Recognition
and Understanding Workshop (ASRU 2013), 2013.
mla: Walter, Oliver, et al. “Hierarchical System for Word Discovery Exploiting DTW-Based
Initialization.” Automatic Speech Recognition and Understanding Workshop (ASRU
2013), 2013.
short: 'O. Walter, T. Korthals, R. Haeb-Umbach, B. Raj, in: Automatic Speech Recognition
and Understanding Workshop (ASRU 2013), 2013.'
date_created: 2019-07-12T05:30:53Z
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/2013/WaKoHaRa13.pdf
oa: '1'
publication: Automatic Speech Recognition and Understanding Workshop (ASRU 2013)
related_material:
link:
- description: Award
relation: supplementary_material
url: https://groups.uni-paderborn.de/nt/pubs/2013/WaKoHaRa13_Award.pdf
- description: Poster
relation: supplementary_material
url: https://groups.uni-paderborn.de/nt/pubs/2013/WaKoHaRa13_Poster.pdf
status: public
title: Hierarchical System for Word Discovery Exploiting DTW-Based Initialization
type: conference
user_id: '44006'
year: '2013'
...
---
_id: '11926'
abstract:
- lang: eng
text: In this paper we present a novel initialization method for unsupervised learning
of acoustic patterns in recordings of continuous speech. The pattern discovery
task is solved by dynamic time warping whose performance we improve by a smart
starting point selection. This enables a more accurate discovery of patterns compared
to conventional approaches. After graph-based clustering the patterns are employed
for training hidden Markov models for an unsupervised speech acquisition. By iterating
between model training and decoding in an EM-like framework the word accuracy
is continuously improved. On the TIDIGITS corpus we achieve a word error rate
of about 13 percent by the proposed unsupervised pattern discovery approach, which
neither assumes knowledge of the acoustic units nor of the labels of the training
data.
author:
- first_name: Oliver
full_name: Walter, Oliver
last_name: Walter
- 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: Walter O, Schmalenstroeer J, Haeb-Umbach R. A Novel Initialization Method
for Unsupervised Learning of Acoustic Patterns in Speech (FGNT-2013-01).;
2013.
apa: Walter, O., Schmalenstroeer, J., & Haeb-Umbach, R. (2013). A Novel Initialization
Method for Unsupervised Learning of Acoustic Patterns in Speech (FGNT-2013-01).
bibtex: '@book{Walter_Schmalenstroeer_Haeb-Umbach_2013, title={A Novel Initialization
Method for Unsupervised Learning of Acoustic Patterns in Speech (FGNT-2013-01)},
author={Walter, Oliver and Schmalenstroeer, Joerg and Haeb-Umbach, Reinhold},
year={2013} }'
chicago: Walter, Oliver, Joerg Schmalenstroeer, and Reinhold Haeb-Umbach. A Novel
Initialization Method for Unsupervised Learning of Acoustic Patterns in Speech
(FGNT-2013-01), 2013.
ieee: O. Walter, J. Schmalenstroeer, and R. Haeb-Umbach, A Novel Initialization
Method for Unsupervised Learning of Acoustic Patterns in Speech (FGNT-2013-01).
2013.
mla: Walter, Oliver, et al. A Novel Initialization Method for Unsupervised Learning
of Acoustic Patterns in Speech (FGNT-2013-01). 2013.
short: O. Walter, J. Schmalenstroeer, R. Haeb-Umbach, A Novel Initialization Method
for Unsupervised Learning of Acoustic Patterns in Speech (FGNT-2013-01), 2013.
date_created: 2019-07-12T05:30:55Z
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/2013/WaScHa2013.pdf
oa: '1'
status: public
title: A Novel Initialization Method for Unsupervised Learning of Acoustic Patterns
in Speech (FGNT-2013-01)
type: report
user_id: '44006'
year: '2013'
...
---
_id: '11832'
abstract:
- lang: eng
text: In this paper we propose an approach to retrieve the absolute geometry of
an acoustic sensor network, consisting of spatially distributed microphone arrays,
from reverberant speech input. The calibration relies on direction of arrival
measurements of the individual arrays. The proposed calibration algorithm is derived
from a maximum-likelihood approach employing circular statistics. Since a sensor
node consists of a microphone array with known intra-array geometry, we are able
to obtain an absolute geometry estimate, including angles and distances. Simulation
results demonstrate the effectiveness of the approach.
author:
- first_name: Florian
full_name: Jacob, Florian
last_name: Jacob
- 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: 'Jacob F, Schmalenstroeer J, Haeb-Umbach R. DoA-Based Microphone Array Position
Self-Calibration Using Circular Statistic. In: 38th International Conference
on Acoustics, Speech, and Signal Processing (ICASSP 2013). ; 2013:116-120.
doi:10.1109/ICASSP.2013.6637620'
apa: Jacob, F., Schmalenstroeer, J., & Haeb-Umbach, R. (2013). DoA-Based Microphone
Array Position Self-Calibration Using Circular Statistic. 38th International
Conference on Acoustics, Speech, and Signal Processing (ICASSP 2013), 116–120.
https://doi.org/10.1109/ICASSP.2013.6637620
bibtex: '@inproceedings{Jacob_Schmalenstroeer_Haeb-Umbach_2013, title={DoA-Based
Microphone Array Position Self-Calibration Using Circular Statistic}, DOI={10.1109/ICASSP.2013.6637620},
booktitle={38th International Conference on Acoustics, Speech, and Signal Processing
(ICASSP 2013)}, author={Jacob, Florian and Schmalenstroeer, Joerg and Haeb-Umbach,
Reinhold}, year={2013}, pages={116–120} }'
chicago: Jacob, Florian, Joerg Schmalenstroeer, and Reinhold Haeb-Umbach. “DoA-Based
Microphone Array Position Self-Calibration Using Circular Statistic.” In 38th
International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2013),
116–20, 2013. https://doi.org/10.1109/ICASSP.2013.6637620.
ieee: 'F. Jacob, J. Schmalenstroeer, and R. Haeb-Umbach, “DoA-Based Microphone Array
Position Self-Calibration Using Circular Statistic,” in 38th International
Conference on Acoustics, Speech, and Signal Processing (ICASSP 2013), 2013,
pp. 116–120, doi: 10.1109/ICASSP.2013.6637620.'
mla: Jacob, Florian, et al. “DoA-Based Microphone Array Position Self-Calibration
Using Circular Statistic.” 38th International Conference on Acoustics, Speech,
and Signal Processing (ICASSP 2013), 2013, pp. 116–20, doi:10.1109/ICASSP.2013.6637620.
short: 'F. Jacob, J. Schmalenstroeer, R. Haeb-Umbach, in: 38th International Conference
on Acoustics, Speech, and Signal Processing (ICASSP 2013), 2013, pp. 116–120.'
date_created: 2019-07-12T05:29:07Z
date_updated: 2023-10-26T08:11:12Z
department:
- _id: '54'
doi: 10.1109/ICASSP.2013.6637620
keyword:
- Geometry calibration
- microphone arrays
- position self-calibration
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://groups.uni-paderborn.de/nt/pubs/2013/JacSchHae_ICASSP2013_Rev2.pdf
oa: '1'
page: 116-120
publication: 38th International Conference on Acoustics, Speech, and Signal Processing
(ICASSP 2013)
publication_identifier:
issn:
- 1520-6149
quality_controlled: '1'
related_material:
link:
- description: Presentation
relation: supplementary_material
url: https://groups.uni-paderborn.de/nt/pubs/2013/JaScHa13_Presentation.pdf
status: public
title: DoA-Based Microphone Array Position Self-Calibration Using Circular Statistic
type: conference
user_id: '460'
year: '2013'
...
---
_id: '11891'
abstract:
- lang: eng
text: In this paper we present a combined hardware/software approach for synchronizing
the sampling clocks of an acoustic sensor network. A first clock frequency offset
estimate is obtained by a time stamp exchange protocol with a low data rate and
computational requirements. The estimate is then postprocessed by a Kalman filter
which exploits the specific properties of the statistics of the frequency offset
estimation error. In long term experiments the deviation between the sampling
oscillators of two sensor nodes never exceeded half a sample with a wired and
with a wireless link between the nodes. The achieved precision enables the estimation
of time difference of arrival values across different hardware devices without
sharing a common sampling hardware.
author:
- 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: 'Schmalenstroeer J, Haeb-Umbach R. Sampling Rate Synchronisation in Acoustic
Sensor Networks with a Pre-Trained Clock Skew Error Model. In: 21th European
Signal Processing Conference (EUSIPCO 2013). ; 2013.'
apa: Schmalenstroeer, J., & Haeb-Umbach, R. (2013). Sampling Rate Synchronisation
in Acoustic Sensor Networks with a Pre-Trained Clock Skew Error Model. 21th
European Signal Processing Conference (EUSIPCO 2013).
bibtex: '@inproceedings{Schmalenstroeer_Haeb-Umbach_2013, title={Sampling Rate Synchronisation
in Acoustic Sensor Networks with a Pre-Trained Clock Skew Error Model}, booktitle={21th
European Signal Processing Conference (EUSIPCO 2013)}, author={Schmalenstroeer,
Joerg and Haeb-Umbach, Reinhold}, year={2013} }'
chicago: Schmalenstroeer, Joerg, and Reinhold Haeb-Umbach. “Sampling Rate Synchronisation
in Acoustic Sensor Networks with a Pre-Trained Clock Skew Error Model.” In 21th
European Signal Processing Conference (EUSIPCO 2013), 2013.
ieee: J. Schmalenstroeer and R. Haeb-Umbach, “Sampling Rate Synchronisation in Acoustic
Sensor Networks with a Pre-Trained Clock Skew Error Model,” 2013.
mla: Schmalenstroeer, Joerg, and Reinhold Haeb-Umbach. “Sampling Rate Synchronisation
in Acoustic Sensor Networks with a Pre-Trained Clock Skew Error Model.” 21th
European Signal Processing Conference (EUSIPCO 2013), 2013.
short: 'J. Schmalenstroeer, R. Haeb-Umbach, in: 21th European Signal Processing
Conference (EUSIPCO 2013), 2013.'
date_created: 2019-07-12T05:30:15Z
date_updated: 2023-10-26T08:11:01Z
department:
- _id: '54'
keyword:
- synchronization
- acoustic sensor network
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://groups.uni-paderborn.de/nt/pubs/2013/SchHaeb2013.pdf
oa: '1'
publication: 21th European Signal Processing Conference (EUSIPCO 2013)
quality_controlled: '1'
related_material:
link:
- description: Presentation
relation: supplementary_material
url: https://groups.uni-paderborn.de/nt/pubs/2013/SchHaeb2013_Presentation.pdf
status: public
title: Sampling Rate Synchronisation in Acoustic Sensor Networks with a Pre-Trained
Clock Skew Error Model
type: conference
user_id: '460'
year: '2013'
...
---
_id: '11818'
abstract:
- lang: eng
text: In this paper we present a system for indoor navigation based on received
signal strength index information of Wireless-LAN access points and relative position
estimates. The relative position information is gathered from inertial smartphone
sensors using a step detection and an orientation estimate. Our map data is hosted
on a server employing a map renderer and a SQL database. The database includes
a complete multilevel office building, within which the user can navigate. During
navigation, the client retrieves the position estimate from the server, together
with the corresponding map tiles to visualize the user's position on the smartphone
display.
author:
- first_name: Manh Kha
full_name: Hoang, Manh Kha
last_name: Hoang
- first_name: Sarah
full_name: Schmitz, Sarah
last_name: Schmitz
- first_name: Christian
full_name: Drueke, Christian
last_name: Drueke
- first_name: Dang Hai Tran
full_name: Vu, Dang Hai Tran
last_name: Vu
- 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, Schmitz S, Drueke C, Vu DHT, Schmalenstroeer J, Haeb-Umbach R. Server
based indoor navigation using RSSI and inertial sensor information. In: Positioning
Navigation and Communication (WPNC), 2013 10th Workshop On. ; 2013:1-6. doi:10.1109/WPNC.2013.6533263'
apa: Hoang, M. K., Schmitz, S., Drueke, C., Vu, D. H. T., Schmalenstroeer, J., &
Haeb-Umbach, R. (2013). Server based indoor navigation using RSSI and inertial
sensor information. Positioning Navigation and Communication (WPNC), 2013 10th
Workshop On, 1–6. https://doi.org/10.1109/WPNC.2013.6533263
bibtex: '@inproceedings{Hoang_Schmitz_Drueke_Vu_Schmalenstroeer_Haeb-Umbach_2013,
title={Server based indoor navigation using RSSI and inertial sensor information},
DOI={10.1109/WPNC.2013.6533263},
booktitle={Positioning Navigation and Communication (WPNC), 2013 10th Workshop
on}, author={Hoang, Manh Kha and Schmitz, Sarah and Drueke, Christian and Vu,
Dang Hai Tran and Schmalenstroeer, Joerg and Haeb-Umbach, Reinhold}, year={2013},
pages={1–6} }'
chicago: Hoang, Manh Kha, Sarah Schmitz, Christian Drueke, Dang Hai Tran Vu, Joerg
Schmalenstroeer, and Reinhold Haeb-Umbach. “Server Based Indoor Navigation Using
RSSI and Inertial Sensor Information.” In Positioning Navigation and Communication
(WPNC), 2013 10th Workshop On, 1–6, 2013. https://doi.org/10.1109/WPNC.2013.6533263.
ieee: 'M. K. Hoang, S. Schmitz, C. Drueke, D. H. T. Vu, J. Schmalenstroeer, and
R. Haeb-Umbach, “Server based indoor navigation using RSSI and inertial sensor
information,” in Positioning Navigation and Communication (WPNC), 2013 10th
Workshop on, 2013, pp. 1–6, doi: 10.1109/WPNC.2013.6533263.'
mla: Hoang, Manh Kha, et al. “Server Based Indoor Navigation Using RSSI and Inertial
Sensor Information.” Positioning Navigation and Communication (WPNC), 2013
10th Workshop On, 2013, pp. 1–6, doi:10.1109/WPNC.2013.6533263.
short: 'M.K. Hoang, S. Schmitz, C. Drueke, D.H.T. Vu, J. Schmalenstroeer, R. Haeb-Umbach,
in: Positioning Navigation and Communication (WPNC), 2013 10th Workshop On, 2013,
pp. 1–6.'
date_created: 2019-07-12T05:28:51Z
date_updated: 2023-10-26T08:09:36Z
department:
- _id: '54'
doi: 10.1109/WPNC.2013.6533263
keyword:
- SQL
- navigation
- smart phones
- wireless LAN
- RSSI
- SQL database
- complete multilevel office building
- inertial sensor information
- inertial smartphone sensors
- map renderer
- received signal strength index information
- relative position estimates
- server based indoor navigation
- step detection
- wireless-LAN access points
- Smartphone
- fingerprint
- indoor navigation
- map tile
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://groups.uni-paderborn.de/nt/pubs/2013/HoScDrTrScHa2013.pdf
oa: '1'
page: 1-6
publication: Positioning Navigation and Communication (WPNC), 2013 10th Workshop on
quality_controlled: '1'
related_material:
link:
- description: Poster
relation: supplementary_material
url: https://groups.uni-paderborn.de/nt/pubs/2013/HoScDrTrScHa2013_Poster.pdf
status: public
title: Server based indoor navigation using RSSI and inertial sensor information
type: conference
user_id: '460'
year: '2013'
...
---
_id: '11817'
abstract:
- lang: eng
text: In this paper we present a modified hidden Markov model (HMM) for the fusion
of received signal strength index (RSSI) information of WiFi access points and
relative position information which is obtained from the inertial sensors of a
smartphone for indoor positioning. Since the states of the HMM represent the potential
user locations, their number determines the quantization error introduced by discretizing
the allowable user positions through the use of the HMM. To reduce this quantization
error we introduce â??pseudoâ?? states, whose emission probability, which models
the RSSI measurements at this location, is synthesized from those of the neighboring
states of which a Gaussian emission probability has been estimated during the
training phase. The experimental results demonstrate the effectiveness of this
approach. By introducing on average two pseudo states per original HMM state the
positioning error could be significantly reduced without increasing the training
effort.
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: Christian
full_name: Drueke, Christian
last_name: Drueke
- 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: 'Hoang MK, Schmalenstroeer J, Drueke C, Tran Vu DH, Haeb-Umbach R. A Hidden
Markov Model for Indoor User Tracking Based on WiFi Fingerprinting and Step Detection.
In: 21th European Signal Processing Conference (EUSIPCO 2013). ; 2013.'
apa: Hoang, M. K., Schmalenstroeer, J., Drueke, C., Tran Vu, D. H., & Haeb-Umbach,
R. (2013). A Hidden Markov Model for Indoor User Tracking Based on WiFi Fingerprinting
and Step Detection. 21th European Signal Processing Conference (EUSIPCO 2013).
bibtex: '@inproceedings{Hoang_Schmalenstroeer_Drueke_Tran Vu_Haeb-Umbach_2013, title={A
Hidden Markov Model for Indoor User Tracking Based on WiFi Fingerprinting and
Step Detection}, booktitle={21th European Signal Processing Conference (EUSIPCO
2013)}, author={Hoang, Manh Kha and Schmalenstroeer, Joerg and Drueke, Christian
and Tran Vu, Dang Hai and Haeb-Umbach, Reinhold}, year={2013} }'
chicago: Hoang, Manh Kha, Joerg Schmalenstroeer, Christian Drueke, Dang Hai Tran
Vu, and Reinhold Haeb-Umbach. “A Hidden Markov Model for Indoor User Tracking
Based on WiFi Fingerprinting and Step Detection.” In 21th European Signal Processing
Conference (EUSIPCO 2013), 2013.
ieee: M. K. Hoang, J. Schmalenstroeer, C. Drueke, D. H. Tran Vu, and R. Haeb-Umbach,
“A Hidden Markov Model for Indoor User Tracking Based on WiFi Fingerprinting and
Step Detection,” 2013.
mla: Hoang, Manh Kha, et al. “A Hidden Markov Model for Indoor User Tracking Based
on WiFi Fingerprinting and Step Detection.” 21th European Signal Processing
Conference (EUSIPCO 2013), 2013.
short: 'M.K. Hoang, J. Schmalenstroeer, C. Drueke, D.H. Tran Vu, R. Haeb-Umbach,
in: 21th European Signal Processing Conference (EUSIPCO 2013), 2013.'
date_created: 2019-07-12T05:28:50Z
date_updated: 2023-10-26T08:09:45Z
department:
- _id: '54'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://groups.uni-paderborn.de/nt/pubs/2013/HoScDrTrHa2013.pdf
oa: '1'
publication: 21th European Signal Processing Conference (EUSIPCO 2013)
quality_controlled: '1'
related_material:
link:
- description: Poster
relation: supplementary_material
url: https://groups.uni-paderborn.de/nt/pubs/2013/HoScDrTrHa2013_Poster.pdf
status: public
title: A Hidden Markov Model for Indoor User Tracking Based on WiFi Fingerprinting
and Step Detection
type: conference
user_id: '460'
year: '2013'
...
---
_id: '11741'
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. Quality Analysis and Optimization of the MAP-based
Noise Power Spectral Density Tracker. In: Speech Communication; 10. ITG Symposium;
Proceedings. ; 2012.'
apa: Chinaev, A., & Haeb-Umbach, R. (2012). Quality Analysis and Optimization
of the MAP-based Noise Power Spectral Density Tracker. In Speech Communication;
10. ITG Symposium; Proceedings.
bibtex: '@inproceedings{Chinaev_Haeb-Umbach_2012, title={Quality Analysis and Optimization
of the MAP-based Noise Power Spectral Density Tracker}, booktitle={Speech Communication;
10. ITG Symposium; Proceedings.}, author={Chinaev, Aleksej and Haeb-Umbach, Reinhold},
year={2012} }'
chicago: Chinaev, Aleksej, and Reinhold Haeb-Umbach. “Quality Analysis and Optimization
of the MAP-Based Noise Power Spectral Density Tracker.” In Speech Communication;
10. ITG Symposium; Proceedings., 2012.
ieee: A. Chinaev and R. Haeb-Umbach, “Quality Analysis and Optimization of the MAP-based
Noise Power Spectral Density Tracker,” in Speech Communication; 10. ITG Symposium;
Proceedings., 2012.
mla: Chinaev, Aleksej, and Reinhold Haeb-Umbach. “Quality Analysis and Optimization
of the MAP-Based Noise Power Spectral Density Tracker.” Speech Communication;
10. ITG Symposium; Proceedings., 2012.
short: 'A. Chinaev, R. Haeb-Umbach, in: Speech Communication; 10. ITG Symposium;
Proceedings., 2012.'
date_created: 2019-07-12T05:27:22Z
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/2012/ChHa12.pdf
oa: '1'
publication: Speech Communication; 10. ITG Symposium; Proceedings.
related_material:
link:
- description: Poster
relation: supplementary_material
url: https://groups.uni-paderborn.de/nt/pubs/2012/ChHa12_Poster.pdf
status: public
title: Quality Analysis and Optimization of the MAP-based Noise Power Spectral Density
Tracker
type: conference
user_id: '44006'
year: '2012'
...
---
_id: '11745'
abstract:
- lang: eng
text: In this paper we present a novel noise power spectral density tracking algorithm
and its use in single-channel speech enhancement. It has the unique feature that
it is able to track the noise statistics even if speech is dominant in a given
time-frequency bin. As a consequence it can follow non-stationary noise superposed
by speech, even in the critical case of rising noise power. The algorithm requires
an initial estimate of the power spectrum of speech and is thus meant to be used
as a postprocessor to a first speech enhancement stage. An experimental comparison
with a state-of-the-art noise tracking algorithm demonstrates lower estimation
errors under low SNR conditions and smaller fluctuations of the estimated values,
resulting in improved speech quality as measured by PESQ scores.
author:
- first_name: Aleksej
full_name: Chinaev, Aleksej
last_name: Chinaev
- first_name: Alexander
full_name: Krueger, Alexander
last_name: Krueger
- 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: 'Chinaev A, Krueger A, Tran Vu DH, Haeb-Umbach R. Improved Noise Power Spectral
Density Tracking by a MAP-based Postprocessor. In: 37th International Conference
on Acoustics, Speech and Signal Processing (ICASSP 2012). ; 2012.'
apa: Chinaev, A., Krueger, A., Tran Vu, D. H., & Haeb-Umbach, R. (2012). Improved
Noise Power Spectral Density Tracking by a MAP-based Postprocessor. In 37th
International Conference on Acoustics, Speech and Signal Processing (ICASSP 2012).
bibtex: '@inproceedings{Chinaev_Krueger_Tran Vu_Haeb-Umbach_2012, title={Improved
Noise Power Spectral Density Tracking by a MAP-based Postprocessor}, booktitle={37th
International Conference on Acoustics, Speech and Signal Processing (ICASSP 2012)},
author={Chinaev, Aleksej and Krueger, Alexander and Tran Vu, Dang Hai and Haeb-Umbach,
Reinhold}, year={2012} }'
chicago: Chinaev, Aleksej, Alexander Krueger, Dang Hai Tran Vu, and Reinhold Haeb-Umbach.
“Improved Noise Power Spectral Density Tracking by a MAP-Based Postprocessor.”
In 37th International Conference on Acoustics, Speech and Signal Processing
(ICASSP 2012), 2012.
ieee: A. Chinaev, A. Krueger, D. H. Tran Vu, and R. Haeb-Umbach, “Improved Noise
Power Spectral Density Tracking by a MAP-based Postprocessor,” in 37th International
Conference on Acoustics, Speech and Signal Processing (ICASSP 2012), 2012.
mla: Chinaev, Aleksej, et al. “Improved Noise Power Spectral Density Tracking by
a MAP-Based Postprocessor.” 37th International Conference on Acoustics, Speech
and Signal Processing (ICASSP 2012), 2012.
short: 'A. Chinaev, A. Krueger, D.H. Tran Vu, R. Haeb-Umbach, in: 37th International
Conference on Acoustics, Speech and Signal Processing (ICASSP 2012), 2012.'
date_created: 2019-07-12T05:27:26Z
date_updated: 2022-01-06T06:51:08Z
department:
- _id: '54'
keyword:
- MAP parameter estimation
- noise power estimation
- speech enhancement
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://groups.uni-paderborn.de/nt/pubs/2012/ChKrDaHa12.pdf
oa: '1'
publication: 37th International Conference on Acoustics, Speech and Signal Processing
(ICASSP 2012)
related_material:
link:
- description: Presentation
relation: supplementary_material
url: https://groups.uni-paderborn.de/nt/pubs/2012/ChKrDaHa12_Talk.pdf
status: public
title: Improved Noise Power Spectral Density Tracking by a MAP-based Postprocessor
type: conference
user_id: '44006'
year: '2012'
...
---
_id: '11844'
author:
- 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: 'Krueger A, Haeb-Umbach R. Reverberant Speech Recognition. In: Techniques
for Noise Robustness in Automatic Speech Recognition. Wiley; 2012.'
apa: Krueger, A., & Haeb-Umbach, R. (2012). Reverberant Speech Recognition.
In Techniques for Noise Robustness in Automatic Speech Recognition. Wiley.
bibtex: '@inbook{Krueger_Haeb-Umbach_2012, title={Reverberant Speech Recognition},
booktitle={Techniques for Noise Robustness in Automatic Speech Recognition}, publisher={Wiley},
author={Krueger, Alexander and Haeb-Umbach, Reinhold}, year={2012} }'
chicago: Krueger, Alexander, and Reinhold Haeb-Umbach. “Reverberant Speech Recognition.”
In Techniques for Noise Robustness in Automatic Speech Recognition. Wiley,
2012.
ieee: A. Krueger and R. Haeb-Umbach, “Reverberant Speech Recognition,” in Techniques
for Noise Robustness in Automatic Speech Recognition, Wiley, 2012.
mla: Krueger, Alexander, and Reinhold Haeb-Umbach. “Reverberant Speech Recognition.”
Techniques for Noise Robustness in Automatic Speech Recognition, Wiley,
2012.
short: 'A. Krueger, R. Haeb-Umbach, in: Techniques for Noise Robustness in Automatic
Speech Recognition, Wiley, 2012.'
date_created: 2019-07-12T05:29:21Z
date_updated: 2022-01-06T06:51:11Z
department:
- _id: '54'
language:
- iso: eng
publication: Techniques for Noise Robustness in Automatic Speech Recognition
publisher: Wiley
status: public
title: Reverberant Speech Recognition
type: book_chapter
user_id: '44006'
year: '2012'
...