---
_id: '13271'
abstract:
- lang: eng
text: Automatic meeting analysis comprises the tasks of speaker counting, speaker
diarization, and the separation of overlapped speech, followed by automatic speech
recognition. This all has to be carried out on arbitrarily long sessions and,
ideally, in an online or block-online manner. While significant progress has been
made on individual tasks, this paper presents for the first time an all-neural
approach to simultaneous speaker counting, diarization and source separation.
The NN-based estimator operates in a block-online fashion and tracks speakers
even if they remain silent for a number of time blocks, thus learning a stable
output order for the separated sources. The neural network is recurrent over time
as well as over the number of sources. The simulation experiments show that state
of the art separation performance is achieved, while at the same time delivering
good diarization and source counting results. It even generalizes well to an unseen
large number of blocks.
author:
- first_name: Thilo
full_name: von Neumann, Thilo
last_name: von Neumann
- first_name: Keisuke
full_name: Kinoshita, Keisuke
last_name: Kinoshita
- first_name: Marc
full_name: Delcroix, Marc
last_name: Delcroix
- first_name: Shoko
full_name: Araki, Shoko
last_name: Araki
- first_name: Tomohiro
full_name: Nakatani, Tomohiro
last_name: Nakatani
- first_name: Reinhold
full_name: Haeb-Umbach, Reinhold
id: '242'
last_name: Haeb-Umbach
citation:
ama: 'von Neumann T, Kinoshita K, Delcroix M, Araki S, Nakatani T, Haeb-Umbach R.
All-neural Online Source Separation, Counting, and Diarization for Meeting Analysis.
In: ICASSP 2019, Brighton, UK. ; 2019.'
apa: von Neumann, T., Kinoshita, K., Delcroix, M., Araki, S., Nakatani, T., &
Haeb-Umbach, R. (2019). All-neural Online Source Separation, Counting, and Diarization
for Meeting Analysis. In ICASSP 2019, Brighton, UK.
bibtex: '@inproceedings{von Neumann_Kinoshita_Delcroix_Araki_Nakatani_Haeb-Umbach_2019,
title={All-neural Online Source Separation, Counting, and Diarization for Meeting
Analysis}, booktitle={ICASSP 2019, Brighton, UK}, author={von Neumann, Thilo and
Kinoshita, Keisuke and Delcroix, Marc and Araki, Shoko and Nakatani, Tomohiro
and Haeb-Umbach, Reinhold}, year={2019} }'
chicago: Neumann, Thilo von, Keisuke Kinoshita, Marc Delcroix, Shoko Araki, Tomohiro
Nakatani, and Reinhold Haeb-Umbach. “All-Neural Online Source Separation, Counting,
and Diarization for Meeting Analysis.” In ICASSP 2019, Brighton, UK, 2019.
ieee: T. von Neumann, K. Kinoshita, M. Delcroix, S. Araki, T. Nakatani, and R. Haeb-Umbach,
“All-neural Online Source Separation, Counting, and Diarization for Meeting Analysis,”
in ICASSP 2019, Brighton, UK, 2019.
mla: von Neumann, Thilo, et al. “All-Neural Online Source Separation, Counting,
and Diarization for Meeting Analysis.” ICASSP 2019, Brighton, UK, 2019.
short: 'T. von Neumann, K. Kinoshita, M. Delcroix, S. Araki, T. Nakatani, R. Haeb-Umbach,
in: ICASSP 2019, Brighton, UK, 2019.'
date_created: 2019-09-18T08:20:50Z
date_updated: 2022-01-06T06:51:31Z
ddc:
- '000'
department:
- _id: '54'
file:
- access_level: open_access
content_type: application/pdf
creator: huesera
date_created: 2019-09-18T08:28:39Z
date_updated: 2019-09-19T07:05:57Z
file_id: '13272'
file_name: ICASSP_2019_Neumann_Paper.pdf
file_size: 126453
relation: main_file
file_date_updated: 2019-09-19T07:05:57Z
has_accepted_license: '1'
language:
- iso: eng
oa: '1'
publication: ICASSP 2019, Brighton, UK
status: public
title: All-neural Online Source Separation, Counting, and Diarization for Meeting
Analysis
type: conference
user_id: '59789'
year: '2019'
...
---
_id: '15814'
abstract:
- lang: eng
text: Once a popular theme of futuristic science fiction or far-fetched technology
forecasts, digital home assistants with a spoken language interface have become
a ubiquitous commodity today. This success has been made possible by major advancements
in signal processing and machine learning for so-called far-field speech recognition,
where the commands are spoken at a distance from the sound capturing device. The
challenges encountered are quite unique and different from many other use cases
of automatic speech recognition. The purpose of this tutorial article is to describe,
in a way amenable to the non-specialist, the key speech processing algorithms
that enable reliable fully hands-free speech interaction with digital home assistants.
These technologies include multi-channel acoustic echo cancellation, microphone
array processing and dereverberation techniques for signal enhancement, reliable
wake-up word and end-of-interaction detection, high-quality speech synthesis,
as well as sophisticated statistical models for speech and language, learned from
large amounts of heterogeneous training data. In all these fields, deep learning
has occupied a critical role.
author:
- first_name: Reinhold
full_name: Haeb-Umbach, Reinhold
id: '242'
last_name: Haeb-Umbach
- first_name: Shinji
full_name: Watanabe, Shinji
last_name: Watanabe
- first_name: Tomohiro
full_name: Nakatani, Tomohiro
last_name: Nakatani
- first_name: Michiel
full_name: Bacchiani, Michiel
last_name: Bacchiani
- first_name: Bjoern
full_name: Hoffmeister, Bjoern
last_name: Hoffmeister
- first_name: Michael L.
full_name: Seltzer, Michael L.
last_name: Seltzer
- first_name: Heiga
full_name: Zen, Heiga
last_name: Zen
- first_name: Mehrez
full_name: Souden, Mehrez
last_name: Souden
citation:
ama: 'Haeb-Umbach R, Watanabe S, Nakatani T, et al. Speech Processing for Digital
Home Assistance: Combining Signal Processing With Deep-Learning Techniques. IEEE
Signal Processing Magazine. 2019;36(6):111-124. doi:10.1109/MSP.2019.2918706'
apa: 'Haeb-Umbach, R., Watanabe, S., Nakatani, T., Bacchiani, M., Hoffmeister, B.,
Seltzer, M. L., Zen, H., & Souden, M. (2019). Speech Processing for Digital
Home Assistance: Combining Signal Processing With Deep-Learning Techniques. IEEE
Signal Processing Magazine, 36(6), 111–124. https://doi.org/10.1109/MSP.2019.2918706'
bibtex: '@article{Haeb-Umbach_Watanabe_Nakatani_Bacchiani_Hoffmeister_Seltzer_Zen_Souden_2019,
title={Speech Processing for Digital Home Assistance: Combining Signal Processing
With Deep-Learning Techniques}, volume={36}, DOI={10.1109/MSP.2019.2918706},
number={6}, journal={IEEE Signal Processing Magazine}, author={Haeb-Umbach, Reinhold
and Watanabe, Shinji and Nakatani, Tomohiro and Bacchiani, Michiel and Hoffmeister,
Bjoern and Seltzer, Michael L. and Zen, Heiga and Souden, Mehrez}, year={2019},
pages={111–124} }'
chicago: 'Haeb-Umbach, Reinhold, Shinji Watanabe, Tomohiro Nakatani, Michiel Bacchiani,
Bjoern Hoffmeister, Michael L. Seltzer, Heiga Zen, and Mehrez Souden. “Speech
Processing for Digital Home Assistance: Combining Signal Processing With Deep-Learning
Techniques.” IEEE Signal Processing Magazine 36, no. 6 (2019): 111–24.
https://doi.org/10.1109/MSP.2019.2918706.'
ieee: 'R. Haeb-Umbach et al., “Speech Processing for Digital Home Assistance:
Combining Signal Processing With Deep-Learning Techniques,” IEEE Signal Processing
Magazine, vol. 36, no. 6, pp. 111–124, 2019, doi: 10.1109/MSP.2019.2918706.'
mla: 'Haeb-Umbach, Reinhold, et al. “Speech Processing for Digital Home Assistance:
Combining Signal Processing With Deep-Learning Techniques.” IEEE Signal Processing
Magazine, vol. 36, no. 6, 2019, pp. 111–24, doi:10.1109/MSP.2019.2918706.'
short: R. Haeb-Umbach, S. Watanabe, T. Nakatani, M. Bacchiani, B. Hoffmeister, M.L.
Seltzer, H. Zen, M. Souden, IEEE Signal Processing Magazine 36 (2019) 111–124.
date_created: 2020-02-06T07:26:20Z
date_updated: 2023-01-09T11:47:09Z
ddc:
- '000'
department:
- _id: '54'
doi: 10.1109/MSP.2019.2918706
file:
- access_level: open_access
content_type: application/pdf
creator: huesera
date_created: 2020-02-06T07:28:26Z
date_updated: 2020-02-06T07:28:26Z
file_id: '15815'
file_name: JournalIEEESignal ProcessingMagazine_2019_Haeb-Umbach_Paper.pdf
file_size: 1085002
relation: main_file
file_date_updated: 2020-02-06T07:28:26Z
has_accepted_license: '1'
intvolume: ' 36'
issue: '6'
language:
- iso: eng
oa: '1'
page: 111-124
publication: IEEE Signal Processing Magazine
publication_identifier:
issn:
- 1558-0792
status: public
title: 'Speech Processing for Digital Home Assistance: Combining Signal Processing
With Deep-Learning Techniques'
type: journal_article
user_id: '242'
volume: 36
year: '2019'
...
---
_id: '19450'
abstract:
- lang: eng
text: 'Wenn akustische Signalverarbeitung mit automatisiertem Lernen verknüpft wird:
Nachrichtentechniker arbeiten mit mehreren Mikrofonen und tiefen neuronalen Netzen
an besserer Spracherkennung unter widrigsten Bedingungen. Von solchen Sensornetzwerken
könnten langfristig auch digitale Sprachassistenten profitieren.'
author:
- first_name: Reinhold
full_name: Haeb-Umbach, Reinhold
id: '242'
last_name: Haeb-Umbach
citation:
ama: Haeb-Umbach R. Lektionen für Alexa & Co?! DFG forschung 1/2019.
Published online 2019:12-15. doi:10.1002/fors.201970104
apa: Haeb-Umbach, R. (2019). Lektionen für Alexa & Co?! DFG Forschung 1/2019,
12–15. https://doi.org/10.1002/fors.201970104
bibtex: '@article{Haeb-Umbach_2019, title={Lektionen für Alexa & Co?!}, DOI={10.1002/fors.201970104}, journal={DFG
forschung 1/2019}, author={Haeb-Umbach, Reinhold}, year={2019}, pages={12–15}
}'
chicago: Haeb-Umbach, Reinhold. “Lektionen Für Alexa & Co?!” DFG Forschung
1/2019, 2019, 12–15. https://doi.org/10.1002/fors.201970104.
ieee: 'R. Haeb-Umbach, “Lektionen für Alexa & Co?!,” DFG forschung 1/2019,
pp. 12–15, 2019, doi: 10.1002/fors.201970104.'
mla: Haeb-Umbach, Reinhold. “Lektionen Für Alexa & Co?!” DFG Forschung 1/2019,
2019, pp. 12–15, doi:10.1002/fors.201970104.
short: R. Haeb-Umbach, DFG Forschung 1/2019 (2019) 12–15.
date_created: 2020-09-16T08:09:15Z
date_updated: 2023-01-11T11:24:57Z
ddc:
- '000'
department:
- _id: '54'
doi: 10.1002/fors.201970104
file:
- access_level: open_access
content_type: application/pdf
creator: huesera
date_created: 2020-09-16T08:10:25Z
date_updated: 2020-09-16T08:10:25Z
file_id: '19451'
file_name: Artikel_2019_haeb_umbach.pdf
file_size: 337622
relation: main_file
file_date_updated: 2020-09-16T08:10:25Z
has_accepted_license: '1'
language:
- iso: eng
oa: '1'
page: 12-15
publication: DFG forschung 1/2019
status: public
title: Lektionen für Alexa & Co?!
type: journal_article
user_id: '59789'
year: '2019'
...
---
_id: '15237'
abstract:
- lang: eng
text: This paper presents an approach to voice conversion, whichdoes neither
require parallel data nor speaker or phone labels fortraining. It can convert
between speakers which are not in thetraining set by employing the previously
proposed concept of afactorized hierarchical variational autoencoder. Here, linguisticand
speaker induced variations are separated upon the notionthat content induced variations
change at a much shorter timescale, i.e., at the segment level, than speaker induced
variations,which vary at the longer utterance level. In this contribution wepropose
to employ convolutional instead of recurrent networklayers in the encoder and decoder blocks, which is shown toachieve
better phone recognition accuracy on the latent segmentvariables at frame-level
due to their better temporal resolution.For voice conversion the mean of the utterance
variables is re-placed with the respective estimated mean of the target speaker.The
resulting log-mel spectra of the decoder output are used aslocal conditions of
a WaveNet which is utilized for synthesis ofthe speech waveforms. Experiments
show both good disentan-glement properties of the latent space variables, and
good voiceconversion performance.
author:
- first_name: Tobias
full_name: Gburrek, Tobias
id: '44006'
last_name: Gburrek
- first_name: Thomas
full_name: Glarner, Thomas
id: '14169'
last_name: Glarner
- first_name: Janek
full_name: Ebbers, Janek
id: '34851'
last_name: Ebbers
- first_name: Reinhold
full_name: Haeb-Umbach, Reinhold
id: '242'
last_name: Haeb-Umbach
- first_name: Petra
full_name: Wagner, Petra
last_name: Wagner
citation:
ama: 'Gburrek T, Glarner T, Ebbers J, Haeb-Umbach R, Wagner P. Unsupervised Learning
of a Disentangled Speech Representation for Voice Conversion. In: Proc. 10th
ISCA Speech Synthesis Workshop. ; 2019:81-86. doi:10.21437/SSW.2019-15'
apa: Gburrek, T., Glarner, T., Ebbers, J., Haeb-Umbach, R., & Wagner, P. (2019).
Unsupervised Learning of a Disentangled Speech Representation for Voice Conversion.
Proc. 10th ISCA Speech Synthesis Workshop, 81–86. https://doi.org/10.21437/SSW.2019-15
bibtex: '@inproceedings{Gburrek_Glarner_Ebbers_Haeb-Umbach_Wagner_2019, title={Unsupervised
Learning of a Disentangled Speech Representation for Voice Conversion}, DOI={10.21437/SSW.2019-15}, booktitle={Proc.
10th ISCA Speech Synthesis Workshop}, author={Gburrek, Tobias and Glarner, Thomas
and Ebbers, Janek and Haeb-Umbach, Reinhold and Wagner, Petra}, year={2019}, pages={81–86}
}'
chicago: Gburrek, Tobias, Thomas Glarner, Janek Ebbers, Reinhold Haeb-Umbach, and
Petra Wagner. “Unsupervised Learning of a Disentangled Speech Representation for
Voice Conversion.” In Proc. 10th ISCA Speech Synthesis Workshop, 81–86,
2019. https://doi.org/10.21437/SSW.2019-15.
ieee: 'T. Gburrek, T. Glarner, J. Ebbers, R. Haeb-Umbach, and P. Wagner, “Unsupervised
Learning of a Disentangled Speech Representation for Voice Conversion,” in Proc.
10th ISCA Speech Synthesis Workshop, Vienna, 2019, pp. 81–86, doi: 10.21437/SSW.2019-15.'
mla: Gburrek, Tobias, et al. “Unsupervised Learning of a Disentangled Speech Representation
for Voice Conversion.” Proc. 10th ISCA Speech Synthesis Workshop, 2019,
pp. 81–86, doi:10.21437/SSW.2019-15.
short: 'T. Gburrek, T. Glarner, J. Ebbers, R. Haeb-Umbach, P. Wagner, in: Proc.
10th ISCA Speech Synthesis Workshop, 2019, pp. 81–86.'
conference:
location: Vienna
name: 10th ISCA Speech Synthesis Workshop
date_created: 2019-12-04T08:12:29Z
date_updated: 2023-11-17T06:20:39Z
department:
- _id: '54'
doi: 10.21437/SSW.2019-15
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://www.isca-speech.org/archive/pdfs/ssw_2019/gburrek19_ssw.pdf
oa: '1'
page: 81-86
publication: Proc. 10th ISCA Speech Synthesis Workshop
quality_controlled: '1'
related_material:
link:
- description: Listening examples
relation: supplementary_material
url: http://go.upb.de/vcex
status: public
title: Unsupervised Learning of a Disentangled Speech Representation for Voice Conversion
type: conference
user_id: '44006'
year: '2019'
...
---
_id: '15794'
abstract:
- lang: eng
text: In this paper we present our audio tagging system for the DCASE 2019 Challenge
Task 2. We propose a model consisting of a convolutional front end using log-mel-energies
as input features, a recurrent neural network sequence encoder and a fully connected
classifier network outputting an activity probability for each of the 80 considered
event classes. Due to the recurrent neural network, which encodes a whole sequence
into a single vector, our model is able to process sequences of varying lengths.
The model is trained with only little manually labeled training data and a larger
amount of automatically labeled web data, which hence suffers from label noise.
To efficiently train the model with the provided data we use various data augmentation
to prevent overfitting and improve generalization. Our best submitted system achieves
a label-weighted label-ranking average precision (lwlrap) of 75.5% on the private
test set which is an absolute improvement of 21.7% over the baseline. This system
scored the second place in the teams ranking of the DCASE 2019 Challenge Task
2 and the fifth place in the Kaggle competition “Freesound Audio Tagging 2019”
with more than 400 participants. After the challenge ended we further improved
performance to 76.5% lwlrap setting a new state-of-the-art on this dataset.
author:
- first_name: Janek
full_name: Ebbers, Janek
id: '34851'
last_name: Ebbers
- first_name: Reinhold
full_name: Haeb-Umbach, Reinhold
id: '242'
last_name: Haeb-Umbach
citation:
ama: 'Ebbers J, Haeb-Umbach R. Convolutional Recurrent Neural Network and Data Augmentation
for Audio Tagging with Noisy Labels and Minimal Supervision. In: DCASE2019
Workshop, New York, USA. ; 2019.'
apa: Ebbers, J., & Haeb-Umbach, R. (2019). Convolutional Recurrent Neural Network
and Data Augmentation for Audio Tagging with Noisy Labels and Minimal Supervision.
DCASE2019 Workshop, New York, USA.
bibtex: '@inproceedings{Ebbers_Haeb-Umbach_2019, title={Convolutional Recurrent
Neural Network and Data Augmentation for Audio Tagging with Noisy Labels and Minimal
Supervision}, booktitle={DCASE2019 Workshop, New York, USA}, author={Ebbers, Janek
and Haeb-Umbach, Reinhold}, year={2019} }'
chicago: Ebbers, Janek, and Reinhold Haeb-Umbach. “Convolutional Recurrent Neural
Network and Data Augmentation for Audio Tagging with Noisy Labels and Minimal
Supervision.” In DCASE2019 Workshop, New York, USA, 2019.
ieee: J. Ebbers and R. Haeb-Umbach, “Convolutional Recurrent Neural Network and
Data Augmentation for Audio Tagging with Noisy Labels and Minimal Supervision,”
2019.
mla: Ebbers, Janek, and Reinhold Haeb-Umbach. “Convolutional Recurrent Neural Network
and Data Augmentation for Audio Tagging with Noisy Labels and Minimal Supervision.”
DCASE2019 Workshop, New York, USA, 2019.
short: 'J. Ebbers, R. Haeb-Umbach, in: DCASE2019 Workshop, New York, USA, 2019.'
date_created: 2020-02-05T10:16:03Z
date_updated: 2023-11-22T08:30:12Z
ddc:
- '000'
department:
- _id: '54'
file:
- access_level: open_access
content_type: application/pdf
creator: huesera
date_created: 2020-02-05T10:18:06Z
date_updated: 2020-02-05T10:18:06Z
file_id: '15795'
file_name: DCASE_2019_WS_Ebbers_Paper.pdf
file_size: 184967
relation: main_file
file_date_updated: 2020-02-05T10:18:06Z
has_accepted_license: '1'
language:
- iso: eng
oa: '1'
project:
- _id: '52'
name: Computing Resources Provided by the Paderborn Center for Parallel Computing
publication: DCASE2019 Workshop, New York, USA
quality_controlled: '1'
status: public
title: Convolutional Recurrent Neural Network and Data Augmentation for Audio Tagging
with Noisy Labels and Minimal Supervision
type: conference
user_id: '34851'
year: '2019'
...
---
_id: '15796'
abstract:
- lang: eng
text: In this paper we consider human daily activity recognition using an acoustic
sensor network (ASN) which consists of nodes distributed in a home environment.
Assuming that the ASN is permanently recording, the vast majority of recordings
is silence. Therefore, we propose to employ a computationally efficient two-stage
sound recognition system, consisting of an initial sound activity detection (SAD)
and a subsequent sound event classification (SEC), which is only activated once
sound activity has been detected. We show how a low-latency activity detector
with high temporal resolution can be trained from weak labels with low temporal
resolution. We further demonstrate the advantage of using spatial features for
the subsequent event classification task.
author:
- first_name: Janek
full_name: Ebbers, Janek
id: '34851'
last_name: Ebbers
- first_name: Lukas
full_name: Drude, Lukas
id: '11213'
last_name: Drude
- first_name: Reinhold
full_name: Haeb-Umbach, Reinhold
id: '242'
last_name: Haeb-Umbach
- first_name: Andreas
full_name: Brendel, Andreas
last_name: Brendel
- first_name: Walter
full_name: Kellermann, Walter
last_name: Kellermann
citation:
ama: 'Ebbers J, Drude L, Haeb-Umbach R, Brendel A, Kellermann W. Weakly Supervised
Sound Activity Detection and Event Classification in Acoustic Sensor Networks.
In: CAMSAP 2019, Guadeloupe, West Indies. ; 2019.'
apa: Ebbers, J., Drude, L., Haeb-Umbach, R., Brendel, A., & Kellermann, W. (2019).
Weakly Supervised Sound Activity Detection and Event Classification in Acoustic
Sensor Networks. CAMSAP 2019, Guadeloupe, West Indies.
bibtex: '@inproceedings{Ebbers_Drude_Haeb-Umbach_Brendel_Kellermann_2019, title={Weakly
Supervised Sound Activity Detection and Event Classification in Acoustic Sensor
Networks}, booktitle={CAMSAP 2019, Guadeloupe, West Indies}, author={Ebbers, Janek
and Drude, Lukas and Haeb-Umbach, Reinhold and Brendel, Andreas and Kellermann,
Walter}, year={2019} }'
chicago: Ebbers, Janek, Lukas Drude, Reinhold Haeb-Umbach, Andreas Brendel, and
Walter Kellermann. “Weakly Supervised Sound Activity Detection and Event Classification
in Acoustic Sensor Networks.” In CAMSAP 2019, Guadeloupe, West Indies,
2019.
ieee: J. Ebbers, L. Drude, R. Haeb-Umbach, A. Brendel, and W. Kellermann, “Weakly
Supervised Sound Activity Detection and Event Classification in Acoustic Sensor
Networks,” 2019.
mla: Ebbers, Janek, et al. “Weakly Supervised Sound Activity Detection and Event
Classification in Acoustic Sensor Networks.” CAMSAP 2019, Guadeloupe, West
Indies, 2019.
short: 'J. Ebbers, L. Drude, R. Haeb-Umbach, A. Brendel, W. Kellermann, in: CAMSAP
2019, Guadeloupe, West Indies, 2019.'
date_created: 2020-02-05T10:20:17Z
date_updated: 2023-11-22T08:29:58Z
ddc:
- '000'
department:
- _id: '54'
file:
- access_level: open_access
content_type: application/pdf
creator: huesera
date_created: 2020-02-05T10:21:39Z
date_updated: 2020-02-05T10:21:39Z
file_id: '15797'
file_name: CAMSAP_2019_WS_Ebbers_Paper.pdf
file_size: 311887
relation: main_file
file_date_updated: 2020-02-05T10:21:39Z
has_accepted_license: '1'
language:
- iso: eng
oa: '1'
project:
- _id: '52'
name: Computing Resources Provided by the Paderborn Center for Parallel Computing
publication: CAMSAP 2019, Guadeloupe, West Indies
quality_controlled: '1'
status: public
title: Weakly Supervised Sound Activity Detection and Event Classification in Acoustic
Sensor Networks
type: conference
user_id: '34851'
year: '2019'
...
---
_id: '15792'
abstract:
- lang: eng
text: In this paper we highlight the privacy risks entailed in deep neural network
feature extraction for domestic activity monitoring. We employ the baseline system
proposed in the Task 5 of the DCASE 2018 challenge and simulate a feature interception
attack by an eavesdropper who wants to perform speaker identification. We then
propose to reduce the aforementioned privacy risks by introducing a variational
information feature extraction scheme that allows for good activity monitoring
performance while at the same time minimizing the information of the feature representation,
thus restricting speaker identification attempts. We analyze the resulting model’s
composite loss function and the budget scaling factor used to control the balance
between the performance of the trusted and attacker tasks. It is empirically demonstrated
that the proposed method reduces speaker identification privacy risks without
significantly deprecating the performance of domestic activity monitoring tasks.
author:
- first_name: Alexandru
full_name: Nelus, Alexandru
last_name: Nelus
- first_name: Janek
full_name: Ebbers, Janek
id: '34851'
last_name: Ebbers
- first_name: Reinhold
full_name: Haeb-Umbach, Reinhold
id: '242'
last_name: Haeb-Umbach
- first_name: Rainer
full_name: Martin, Rainer
last_name: Martin
citation:
ama: 'Nelus A, Ebbers J, Haeb-Umbach R, Martin R. Privacy-preserving Variational
Information Feature Extraction for Domestic Activity Monitoring Versus Speaker
Identification. In: INTERSPEECH 2019, Graz, Austria. ; 2019.'
apa: Nelus, A., Ebbers, J., Haeb-Umbach, R., & Martin, R. (2019). Privacy-preserving
Variational Information Feature Extraction for Domestic Activity Monitoring Versus
Speaker Identification. INTERSPEECH 2019, Graz, Austria.
bibtex: '@inproceedings{Nelus_Ebbers_Haeb-Umbach_Martin_2019, title={Privacy-preserving
Variational Information Feature Extraction for Domestic Activity Monitoring Versus
Speaker Identification}, booktitle={INTERSPEECH 2019, Graz, Austria}, author={Nelus,
Alexandru and Ebbers, Janek and Haeb-Umbach, Reinhold and Martin, Rainer}, year={2019}
}'
chicago: Nelus, Alexandru, Janek Ebbers, Reinhold Haeb-Umbach, and Rainer Martin.
“Privacy-Preserving Variational Information Feature Extraction for Domestic Activity
Monitoring Versus Speaker Identification.” In INTERSPEECH 2019, Graz, Austria,
2019.
ieee: A. Nelus, J. Ebbers, R. Haeb-Umbach, and R. Martin, “Privacy-preserving Variational
Information Feature Extraction for Domestic Activity Monitoring Versus Speaker
Identification,” 2019.
mla: Nelus, Alexandru, et al. “Privacy-Preserving Variational Information Feature
Extraction for Domestic Activity Monitoring Versus Speaker Identification.” INTERSPEECH
2019, Graz, Austria, 2019.
short: 'A. Nelus, J. Ebbers, R. Haeb-Umbach, R. Martin, in: INTERSPEECH 2019, Graz,
Austria, 2019.'
date_created: 2020-02-05T10:07:53Z
date_updated: 2023-11-22T08:27:55Z
ddc:
- '000'
department:
- _id: '54'
file:
- access_level: open_access
content_type: application/pdf
creator: huesera
date_created: 2020-02-05T10:11:40Z
date_updated: 2020-02-05T10:11:40Z
file_id: '15793'
file_name: INTERSPEECH_2019_Ebbers_Paper.pdf
file_size: 454600
relation: main_file
file_date_updated: 2020-02-05T10:11:40Z
has_accepted_license: '1'
language:
- iso: eng
oa: '1'
publication: INTERSPEECH 2019, Graz, Austria
quality_controlled: '1'
status: public
title: Privacy-preserving Variational Information Feature Extraction for Domestic
Activity Monitoring Versus Speaker Identification
type: conference
user_id: '34851'
year: '2019'
...
---
_id: '11760'
abstract:
- lang: eng
text: Acoustic event detection, i.e., the task of assigning a human interpretable
label to a segment of audio, has only recently attracted increased interest in
the research community. Driven by the DCASE challenges and the availability of
large-scale audio datasets, the state-of-the-art has progressed rapidly with deep-learning-based
classi- fiers dominating the field. Because several potential use cases favor
a realization on distributed sensor nodes, e.g. ambient assisted living applications,
habitat monitoring or surveillance, we are concerned with two issues here. Firstly
the classification performance of such systems and secondly the computing resources
required to achieve a certain performance considering node level feature extraction.
In this contribution we look at the balance between the two criteria by employing
traditional techniques and different deep learning architectures, including convolutional
and recurrent models in the context of real life everyday audio recordings in
realistic, however challenging, multisource conditions.
author:
- first_name: Janek
full_name: Ebbers, Janek
id: '34851'
last_name: Ebbers
- first_name: Alexandru
full_name: Nelus, Alexandru
last_name: Nelus
- first_name: Rainer
full_name: Martin, Rainer
last_name: Martin
- first_name: Reinhold
full_name: Haeb-Umbach, Reinhold
id: '242'
last_name: Haeb-Umbach
citation:
ama: 'Ebbers J, Nelus A, Martin R, Haeb-Umbach R. Evaluation of Modulation-MFCC
Features and DNN Classification for Acoustic Event Detection. In: DAGA 2018,
München. ; 2018.'
apa: Ebbers, J., Nelus, A., Martin, R., & Haeb-Umbach, R. (2018). Evaluation
of Modulation-MFCC Features and DNN Classification for Acoustic Event Detection.
In DAGA 2018, München.
bibtex: '@inproceedings{Ebbers_Nelus_Martin_Haeb-Umbach_2018, title={Evaluation
of Modulation-MFCC Features and DNN Classification for Acoustic Event Detection},
booktitle={DAGA 2018, München}, author={Ebbers, Janek and Nelus, Alexandru and
Martin, Rainer and Haeb-Umbach, Reinhold}, year={2018} }'
chicago: Ebbers, Janek, Alexandru Nelus, Rainer Martin, and Reinhold Haeb-Umbach.
“Evaluation of Modulation-MFCC Features and DNN Classification for Acoustic Event
Detection.” In DAGA 2018, München, 2018.
ieee: J. Ebbers, A. Nelus, R. Martin, and R. Haeb-Umbach, “Evaluation of Modulation-MFCC
Features and DNN Classification for Acoustic Event Detection,” in DAGA 2018,
München, 2018.
mla: Ebbers, Janek, et al. “Evaluation of Modulation-MFCC Features and DNN Classification
for Acoustic Event Detection.” DAGA 2018, München, 2018.
short: 'J. Ebbers, A. Nelus, R. Martin, R. Haeb-Umbach, in: DAGA 2018, München,
2018.'
date_created: 2019-07-12T05:27:43Z
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/2018/Daga_2018_Ebbers_Paper.pdf
oa: '1'
publication: DAGA 2018, München
status: public
title: Evaluation of Modulation-MFCC Features and DNN Classification for Acoustic
Event Detection
type: conference
user_id: '44006'
year: '2018'
...
---
_id: '11835'
abstract:
- lang: eng
text: Signal dereverberation using the weighted prediction error (WPE) method has
been proven to be an effective means to raise the accuracy of far-field speech
recognition. But in its original formulation, WPE requires multiple iterations
over a sufficiently long utterance, rendering it unsuitable for online low-latency
applications. Recently, two methods have been proposed to overcome this limitation.
One utilizes a neural network to estimate the power spectral density (PSD) of
the target signal and works in a block-online fashion. The other method relies
on a rather simple PSD estimation which smoothes the observed PSD and utilizes
a recursive formulation which enables it to work on a frame-by-frame basis. In
this paper, we integrate a deep neural network (DNN) based estimator into the
recursive frame-online formulation. We evaluate the performance of the recursive
system with different PSD estimators in comparison to the block-online and offline
variant on two distinct corpora. The REVERB challenge data, where the signal is
mainly deteriorated by reverberation, and a database which combines WSJ and VoiceHome
to also consider (directed) noise sources. The results show that although smoothing
works surprisingly well, the more sophisticated DNN based estimator shows promising
improvements and shortens the performance gap between online and offline processing.
author:
- first_name: Jahn
full_name: Heymann, Jahn
id: '9168'
last_name: Heymann
- first_name: Lukas
full_name: Drude, Lukas
id: '11213'
last_name: Drude
- first_name: Reinhold
full_name: Haeb-Umbach, Reinhold
id: '242'
last_name: Haeb-Umbach
- first_name: Keisuke
full_name: Kinoshita, Keisuke
last_name: Kinoshita
- first_name: Tomohiro
full_name: Nakatani, Tomohiro
last_name: Nakatani
citation:
ama: 'Heymann J, Drude L, Haeb-Umbach R, Kinoshita K, Nakatani T. Frame-Online DNN-WPE
Dereverberation. In: IWAENC 2018, Tokio, Japan. ; 2018.'
apa: Heymann, J., Drude, L., Haeb-Umbach, R., Kinoshita, K., & Nakatani, T.
(2018). Frame-Online DNN-WPE Dereverberation. In IWAENC 2018, Tokio, Japan.
bibtex: '@inproceedings{Heymann_Drude_Haeb-Umbach_Kinoshita_Nakatani_2018, title={Frame-Online
DNN-WPE Dereverberation}, booktitle={IWAENC 2018, Tokio, Japan}, author={Heymann,
Jahn and Drude, Lukas and Haeb-Umbach, Reinhold and Kinoshita, Keisuke and Nakatani,
Tomohiro}, year={2018} }'
chicago: Heymann, Jahn, Lukas Drude, Reinhold Haeb-Umbach, Keisuke Kinoshita, and
Tomohiro Nakatani. “Frame-Online DNN-WPE Dereverberation.” In IWAENC 2018,
Tokio, Japan, 2018.
ieee: J. Heymann, L. Drude, R. Haeb-Umbach, K. Kinoshita, and T. Nakatani, “Frame-Online
DNN-WPE Dereverberation,” in IWAENC 2018, Tokio, Japan, 2018.
mla: Heymann, Jahn, et al. “Frame-Online DNN-WPE Dereverberation.” IWAENC 2018,
Tokio, Japan, 2018.
short: 'J. Heymann, L. Drude, R. Haeb-Umbach, K. Kinoshita, T. Nakatani, in: IWAENC
2018, Tokio, Japan, 2018.'
date_created: 2019-07-12T05:29:10Z
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/2018/IWAENC_2018_Heymann_Paper.pdf
oa: '1'
publication: IWAENC 2018, Tokio, Japan
related_material:
link:
- description: Poster
relation: supplementary_material
url: https://groups.uni-paderborn.de/nt/pubs/2018/IWAENC_2018_Heymann_Poster.pdf
status: public
title: Frame-Online DNN-WPE Dereverberation
type: conference
user_id: '44006'
year: '2018'
...
---
_id: '11837'
abstract:
- lang: eng
text: We present a block-online multi-channel front end for automatic speech recognition
in noisy and reverberated environments. It is an online version of our earlier
proposed neural network supported acoustic beamformer, whose coefficients are
calculated from noise and speech spatial covariance matrices which are estimated
utilizing a neural mask estimator. However, the sparsity of speech in the STFT
domain causes problems for the initial beamformer coefficients estimation in some
frequency bins due to lack of speech observations. We propose two methods to mitigate
this issue. The first is to lower the frequency resolution of the STFT, which
comes with the additional advantage of a reduced time window, thus lowering the
latency introduced by block processing. The second approach is to smooth beamforming
coefficients along the frequency axis, thus exploiting their high interfrequency
correlation. With both approaches the gap between offline and block-online beamformer
performance, as measured by the word error rate achieved by a downstream speech
recognizer, is significantly reduced. Experiments are carried out on two copora,
representing noisy (CHiME-4) and noisy reverberant (voiceHome) environments.
author:
- first_name: Jens
full_name: Heitkaemper, Jens
id: '27643'
last_name: Heitkaemper
- first_name: Jahn
full_name: Heymann, Jahn
id: '9168'
last_name: Heymann
- first_name: Reinhold
full_name: Haeb-Umbach, Reinhold
id: '242'
last_name: Haeb-Umbach
citation:
ama: 'Heitkaemper J, Heymann J, Haeb-Umbach R. Smoothing along Frequency in Online
Neural Network Supported Acoustic Beamforming. In: ITG 2018, Oldenburg, Germany.
; 2018.'
apa: Heitkaemper, J., Heymann, J., & Haeb-Umbach, R. (2018). Smoothing along
Frequency in Online Neural Network Supported Acoustic Beamforming. In ITG 2018,
Oldenburg, Germany.
bibtex: '@inproceedings{Heitkaemper_Heymann_Haeb-Umbach_2018, title={Smoothing along
Frequency in Online Neural Network Supported Acoustic Beamforming}, booktitle={ITG
2018, Oldenburg, Germany}, author={Heitkaemper, Jens and Heymann, Jahn and Haeb-Umbach,
Reinhold}, year={2018} }'
chicago: Heitkaemper, Jens, Jahn Heymann, and Reinhold Haeb-Umbach. “Smoothing along
Frequency in Online Neural Network Supported Acoustic Beamforming.” In ITG
2018, Oldenburg, Germany, 2018.
ieee: J. Heitkaemper, J. Heymann, and R. Haeb-Umbach, “Smoothing along Frequency
in Online Neural Network Supported Acoustic Beamforming,” in ITG 2018, Oldenburg,
Germany, 2018.
mla: Heitkaemper, Jens, et al. “Smoothing along Frequency in Online Neural Network
Supported Acoustic Beamforming.” ITG 2018, Oldenburg, Germany, 2018.
short: 'J. Heitkaemper, J. Heymann, R. Haeb-Umbach, in: ITG 2018, Oldenburg, Germany,
2018.'
date_created: 2019-07-12T05:29:13Z
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/2018/ITG_2018_Heitkaemper_Paper.pdf
oa: '1'
publication: ITG 2018, Oldenburg, Germany
related_material:
link:
- description: Slides
relation: supplementary_material
url: https://groups.uni-paderborn.de/nt/pubs/2018/ITG_2018_Heitkaemper_Slides.pdf
status: public
title: Smoothing along Frequency in Online Neural Network Supported Acoustic Beamforming
type: conference
user_id: '44006'
year: '2018'
...
---
_id: '11872'
abstract:
- lang: eng
text: 'The weighted prediction error (WPE) algorithm has proven to be a very successful
dereverberation method for the REVERB challenge. Likewise, neural network based
mask estimation for beamforming demonstrated very good noise suppression in the
CHiME 3 and CHiME 4 challenges. Recently, it has been shown that this estimator
can also be trained to perform dereverberation and denoising jointly. However,
up to now a comparison of a neural beamformer and WPE is still missing, so is
an investigation into a combination of the two. Therefore, we here provide an
extensive evaluation of both and consequently propose variants to integrate deep
neural network based beamforming with WPE. For these integrated variants we identify
a consistent word error rate (WER) reduction on two distinct databases. In particular,
our study shows that deep learning based beamforming benefits from a model-based
dereverberation technique (i.e. WPE) and vice versa. Our key findings are: (a)
Neural beamforming yields the lower WERs in comparison to WPE the more channels
and noise are present. (b) Integration of WPE and a neural beamformer consistently
outperforms all stand-alone systems.'
author:
- first_name: Lukas
full_name: Drude, Lukas
id: '11213'
last_name: Drude
- first_name: Christoph
full_name: Boeddeker, Christoph
id: '40767'
last_name: Boeddeker
- first_name: Jahn
full_name: Heymann, Jahn
id: '9168'
last_name: Heymann
- first_name: Keisuke
full_name: Kinoshita, Keisuke
last_name: Kinoshita
- first_name: Marc
full_name: Delcroix, Marc
last_name: Delcroix
- first_name: Tomohiro
full_name: Nakatani, Tomohiro
last_name: Nakatani
- first_name: Reinhold
full_name: Haeb-Umbach, Reinhold
id: '242'
last_name: Haeb-Umbach
citation:
ama: 'Drude L, Boeddeker C, Heymann J, et al. Integration neural network based beamforming
and weighted prediction error dereverberation. In: INTERSPEECH 2018, Hyderabad,
India. ; 2018.'
apa: Drude, L., Boeddeker, C., Heymann, J., Kinoshita, K., Delcroix, M., Nakatani,
T., & Haeb-Umbach, R. (2018). Integration neural network based beamforming
and weighted prediction error dereverberation. In INTERSPEECH 2018, Hyderabad,
India.
bibtex: '@inproceedings{Drude_Boeddeker_Heymann_Kinoshita_Delcroix_Nakatani_Haeb-Umbach_2018,
title={Integration neural network based beamforming and weighted prediction error
dereverberation}, booktitle={INTERSPEECH 2018, Hyderabad, India}, author={Drude,
Lukas and Boeddeker, Christoph and Heymann, Jahn and Kinoshita, Keisuke and Delcroix,
Marc and Nakatani, Tomohiro and Haeb-Umbach, Reinhold}, year={2018} }'
chicago: Drude, Lukas, Christoph Boeddeker, Jahn Heymann, Keisuke Kinoshita, Marc
Delcroix, Tomohiro Nakatani, and Reinhold Haeb-Umbach. “Integration Neural Network
Based Beamforming and Weighted Prediction Error Dereverberation.” In INTERSPEECH
2018, Hyderabad, India, 2018.
ieee: L. Drude et al., “Integration neural network based beamforming and
weighted prediction error dereverberation,” in INTERSPEECH 2018, Hyderabad,
India, 2018.
mla: Drude, Lukas, et al. “Integration Neural Network Based Beamforming and Weighted
Prediction Error Dereverberation.” INTERSPEECH 2018, Hyderabad, India,
2018.
short: 'L. Drude, C. Boeddeker, J. Heymann, K. Kinoshita, M. Delcroix, T. Nakatani,
R. Haeb-Umbach, in: INTERSPEECH 2018, Hyderabad, India, 2018.'
date_created: 2019-07-12T05:29:53Z
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/2018/INTERSPEECH_2018_Drude_Paper.pdf
oa: '1'
project:
- _id: '52'
name: Computing Resources Provided by the Paderborn Center for Parallel Computing
publication: INTERSPEECH 2018, Hyderabad, India
related_material:
link:
- description: Slides
relation: supplementary_material
url: https://groups.uni-paderborn.de/nt/pubs/2018/INTERSPEECH_2018_Drude_Slides.pdf
status: public
title: Integration neural network based beamforming and weighted prediction error
dereverberation
type: conference
user_id: '40767'
year: '2018'
...
---
_id: '11873'
abstract:
- lang: eng
text: NARA-WPE is a Python software package providing implementations of the weighted
prediction error (WPE) dereverberation algorithm. WPE has been shown to be a highly
effective tool for speech dereverberation, thus improving the perceptual quality
of the signal and improving the recognition performance of downstream automatic
speech recognition (ASR). It is suitable both for single-channel and multi-channel
applications. The package consist of (1) a Numpy implementation which can easily
be integrated into a custom Python toolchain, and (2) a TensorFlow implementation
which allows integration into larger computational graphs and enables backpropagation
through WPE to train more advanced front-ends. This package comprises of an iterative
offline (batch) version, a block-online version, and a frame-online version which
can be used in moderately low latency applications, e.g. digital speech assistants.
author:
- first_name: Lukas
full_name: Drude, Lukas
id: '11213'
last_name: Drude
- first_name: Jahn
full_name: Heymann, Jahn
id: '9168'
last_name: Heymann
- first_name: Christoph
full_name: Boeddeker, Christoph
id: '40767'
last_name: Boeddeker
- first_name: Reinhold
full_name: Haeb-Umbach, Reinhold
id: '242'
last_name: Haeb-Umbach
citation:
ama: 'Drude L, Heymann J, Boeddeker C, Haeb-Umbach R. NARA-WPE: A Python package
for weighted prediction error dereverberation in Numpy and Tensorflow for online
and offline processing. In: ITG 2018, Oldenburg, Germany. ; 2018.'
apa: 'Drude, L., Heymann, J., Boeddeker, C., & Haeb-Umbach, R. (2018). NARA-WPE:
A Python package for weighted prediction error dereverberation in Numpy and Tensorflow
for online and offline processing. In ITG 2018, Oldenburg, Germany.'
bibtex: '@inproceedings{Drude_Heymann_Boeddeker_Haeb-Umbach_2018, title={NARA-WPE:
A Python package for weighted prediction error dereverberation in Numpy and Tensorflow
for online and offline processing}, booktitle={ITG 2018, Oldenburg, Germany},
author={Drude, Lukas and Heymann, Jahn and Boeddeker, Christoph and Haeb-Umbach,
Reinhold}, year={2018} }'
chicago: 'Drude, Lukas, Jahn Heymann, Christoph Boeddeker, and Reinhold Haeb-Umbach.
“NARA-WPE: A Python Package for Weighted Prediction Error Dereverberation in Numpy
and Tensorflow for Online and Offline Processing.” In ITG 2018, Oldenburg,
Germany, 2018.'
ieee: 'L. Drude, J. Heymann, C. Boeddeker, and R. Haeb-Umbach, “NARA-WPE: A Python
package for weighted prediction error dereverberation in Numpy and Tensorflow
for online and offline processing,” in ITG 2018, Oldenburg, Germany, 2018.'
mla: 'Drude, Lukas, et al. “NARA-WPE: A Python Package for Weighted Prediction Error
Dereverberation in Numpy and Tensorflow for Online and Offline Processing.” ITG
2018, Oldenburg, Germany, 2018.'
short: 'L. Drude, J. Heymann, C. Boeddeker, R. Haeb-Umbach, in: ITG 2018, Oldenburg,
Germany, 2018.'
date_created: 2019-07-12T05:29:54Z
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/2018/ITG_2018_Drude_Paper.pdf
oa: '1'
project:
- _id: '52'
name: Computing Resources Provided by the Paderborn Center for Parallel Computing
publication: ITG 2018, Oldenburg, Germany
related_material:
link:
- description: Poster
relation: supplementary_material
url: https://groups.uni-paderborn.de/nt/pubs/2018/ITG_2018_Drude_Poster.pdf
status: public
title: 'NARA-WPE: A Python package for weighted prediction error dereverberation in
Numpy and Tensorflow for online and offline processing'
type: conference
user_id: '40767'
year: '2018'
...
---
_id: '11916'
abstract:
- lang: eng
text: We present an experimental comparison of seven state-of-the-art machine learning
algorithms for the task of semantic analysis of spoken input, with a special emphasis
on applications for dysarthric speech. Dysarthria is a motor speech disorder,
which is characterized by poor articulation of phonemes. In order to cater for
these noncanonical phoneme realizations, we employed an unsupervised learning
approach to estimate the acoustic models for speech recognition, which does not
require a literal transcription of the training data. Even for the subsequent
task of semantic analysis, only weak supervision is employed, whereby the training
utterance is accompanied by a semantic label only, rather than a literal transcription.
Results on two databases, one of them containing dysarthric speech, are presented
showing that Markov logic networks and conditional random fields substantially
outperform other machine learning approaches. Markov logic networks have proved
to be especially robust to recognition errors, which are caused by imprecise articulation
in dysarthric speech.
author:
- first_name: Vladimir
full_name: Despotovic, Vladimir
last_name: Despotovic
- first_name: Oliver
full_name: Walter, Oliver
last_name: Walter
- first_name: Reinhold
full_name: Haeb-Umbach, Reinhold
id: '242'
last_name: Haeb-Umbach
citation:
ama: 'Despotovic V, Walter O, Haeb-Umbach R. Machine learning techniques for semantic
analysis of dysarthric speech: An experimental study. Speech Communication
99 (2018) 242-251 (Elsevier BV). 2018.'
apa: 'Despotovic, V., Walter, O., & Haeb-Umbach, R. (2018). Machine learning
techniques for semantic analysis of dysarthric speech: An experimental study.
Speech Communication 99 (2018) 242-251 (Elsevier B.V.).'
bibtex: '@article{Despotovic_Walter_Haeb-Umbach_2018, title={Machine learning techniques
for semantic analysis of dysarthric speech: An experimental study}, journal={Speech
Communication 99 (2018) 242-251 (Elsevier B.V.)}, author={Despotovic, Vladimir
and Walter, Oliver and Haeb-Umbach, Reinhold}, year={2018} }'
chicago: 'Despotovic, Vladimir, Oliver Walter, and Reinhold Haeb-Umbach. “Machine
Learning Techniques for Semantic Analysis of Dysarthric Speech: An Experimental
Study.” Speech Communication 99 (2018) 242-251 (Elsevier B.V.), 2018.'
ieee: 'V. Despotovic, O. Walter, and R. Haeb-Umbach, “Machine learning techniques
for semantic analysis of dysarthric speech: An experimental study,” Speech
Communication 99 (2018) 242-251 (Elsevier B.V.), 2018.'
mla: 'Despotovic, Vladimir, et al. “Machine Learning Techniques for Semantic Analysis
of Dysarthric Speech: An Experimental Study.” Speech Communication 99 (2018)
242-251 (Elsevier B.V.), 2018.'
short: V. Despotovic, O. Walter, R. Haeb-Umbach, Speech Communication 99 (2018)
242-251 (Elsevier B.V.) (2018).
date_created: 2019-07-12T05:30:44Z
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/2018/SpeechCommunication_2018_Walter_Paper.pdf
oa: '1'
publication: Speech Communication 99 (2018) 242-251 (Elsevier B.V.)
status: public
title: 'Machine learning techniques for semantic analysis of dysarthric speech: An
experimental study'
type: journal_article
user_id: '44006'
year: '2018'
...
---
_id: '12898'
abstract:
- lang: eng
text: Deep clustering (DC) and deep attractor networks (DANs) are a data-driven
way to monaural blind source separation. Both approaches provide astonishing single
channel performance but have not yet been generalized to block-online processing.
When separating speech in a continuous stream with a block-online algorithm, it
needs to be determined in each block which of the output streams belongs to whom.
In this contribution we solve this block permutation problem by introducing an
additional speaker identification embedding to the DAN model structure. We motivate
this model decision by analyzing the embedding topology of DC and DANs and show,
that DC and DANs themselves are not sufficient for speaker identification. This
model structure (a) improves the signal to distortion ratio (SDR) over a DAN baseline
and (b) provides up to 61% and up to 34% relative reduction in permutation error
rate and re-identification error rate compared to an i-vector baseline, respectively.
author:
- first_name: Lukas
full_name: Drude, Lukas
id: '11213'
last_name: Drude
- first_name: Thilo
full_name: von Neumann, Thilo
last_name: von Neumann
- first_name: Reinhold
full_name: Haeb-Umbach, Reinhold
id: '242'
last_name: Haeb-Umbach
citation:
ama: 'Drude L, von Neumann T, Haeb-Umbach R. Deep Attractor Networks for Speaker
Re-Identifikation and Blind Source Separation. In: ICASSP 2018, Calgary, Canada.
; 2018.'
apa: Drude, L., von Neumann, T., & Haeb-Umbach, R. (2018). Deep Attractor Networks
for Speaker Re-Identifikation and Blind Source Separation. In ICASSP 2018,
Calgary, Canada.
bibtex: '@inproceedings{Drude_von Neumann_Haeb-Umbach_2018, title={Deep Attractor
Networks for Speaker Re-Identifikation and Blind Source Separation}, booktitle={ICASSP
2018, Calgary, Canada}, author={Drude, Lukas and von Neumann, Thilo and Haeb-Umbach,
Reinhold}, year={2018} }'
chicago: Drude, Lukas, Thilo von Neumann, and Reinhold Haeb-Umbach. “Deep Attractor
Networks for Speaker Re-Identifikation and Blind Source Separation.” In ICASSP
2018, Calgary, Canada, 2018.
ieee: L. Drude, T. von Neumann, and R. Haeb-Umbach, “Deep Attractor Networks for
Speaker Re-Identifikation and Blind Source Separation,” in ICASSP 2018, Calgary,
Canada, 2018.
mla: Drude, Lukas, et al. “Deep Attractor Networks for Speaker Re-Identifikation
and Blind Source Separation.” ICASSP 2018, Calgary, Canada, 2018.
short: 'L. Drude, T. von Neumann, R. Haeb-Umbach, in: ICASSP 2018, Calgary, Canada,
2018.'
date_created: 2019-07-30T14:22:53Z
date_updated: 2022-01-06T06:51:24Z
department:
- _id: '54'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://groups.uni-paderborn.de/nt/pubs/2018/ICASSP_2018_Drude2_Paper.pdf
oa: '1'
publication: ICASSP 2018, Calgary, Canada
related_material:
link:
- description: Slides
relation: supplementary_material
url: https://groups.uni-paderborn.de/nt/pubs/2018/ICASSP_2018_Drude2_Slides.pdf
status: public
title: Deep Attractor Networks for Speaker Re-Identifikation and Blind Source Separation
type: conference
user_id: '44006'
year: '2018'
...
---
_id: '12900'
abstract:
- lang: eng
text: 'Deep attractor networks (DANs) are a recently introduced method to blindly
separate sources from spectral features of a monaural recording using bidirectional
long short-term memory networks (BLSTMs). Due to the nature of BLSTMs, this is
inherently not online-ready and resorting to operating on blocks yields a block
permutation problem in that the index of each speaker may change between blocks.
We here propose the joint modeling of spatial and spectral features to solve the
block permutation problem and generalize DANs to multi-channel meeting recordings:
The DAN acts as a spectral feature extractor for a subsequent model-based clustering
approach. We first analyze different joint models in batch-processing scenarios
and finally propose a block-online blind source separation algorithm. The efficacy
of the proposed models is demonstrated on reverberant mixtures corrupted by real
recordings of multi-channel background noise. We demonstrate that both the proposed
batch-processing and the proposed block-online system outperform (a) a spatial-only
model with a state-of-the-art frequency permutation solver and (b) a spectral-only
model with an oracle block permutation solver in terms of signal to distortion
ratio (SDR) gains.'
author:
- first_name: Lukas
full_name: Drude, Lukas
id: '11213'
last_name: Drude
- first_name: ' Takuya '
full_name: 'Higuchi,, Takuya '
last_name: Higuchi,
- first_name: 'Keisuke '
full_name: 'Kinoshita, Keisuke '
last_name: Kinoshita
- first_name: 'Tomohiro '
full_name: 'Nakatani, Tomohiro '
last_name: Nakatani
- first_name: Reinhold
full_name: Haeb-Umbach, Reinhold
id: '242'
last_name: Haeb-Umbach
citation:
ama: 'Drude L, Higuchi, Takuya , Kinoshita K, Nakatani T, Haeb-Umbach R. Dual Frequency-
and Block-Permutation Alignment for Deep Learning Based Block-Online Blind Source
Separation. In: ICASSP 2018, Calgary, Canada. ; 2018.'
apa: Drude, L., Higuchi, Takuya , Kinoshita, K., Nakatani, T., & Haeb-Umbach,
R. (2018). Dual Frequency- and Block-Permutation Alignment for Deep Learning Based
Block-Online Blind Source Separation. In ICASSP 2018, Calgary, Canada.
bibtex: '@inproceedings{Drude_Higuchi,_Kinoshita_Nakatani_Haeb-Umbach_2018, title={Dual
Frequency- and Block-Permutation Alignment for Deep Learning Based Block-Online
Blind Source Separation}, booktitle={ICASSP 2018, Calgary, Canada}, author={Drude,
Lukas and Higuchi, Takuya and Kinoshita, Keisuke and Nakatani, Tomohiro and
Haeb-Umbach, Reinhold}, year={2018} }'
chicago: Drude, Lukas, Takuya Higuchi, Keisuke Kinoshita, Tomohiro Nakatani,
and Reinhold Haeb-Umbach. “Dual Frequency- and Block-Permutation Alignment for
Deep Learning Based Block-Online Blind Source Separation.” In ICASSP 2018,
Calgary, Canada, 2018.
ieee: L. Drude, Takuya Higuchi, K. Kinoshita, T. Nakatani, and R. Haeb-Umbach,
“Dual Frequency- and Block-Permutation Alignment for Deep Learning Based Block-Online
Blind Source Separation,” in ICASSP 2018, Calgary, Canada, 2018.
mla: Drude, Lukas, et al. “Dual Frequency- and Block-Permutation Alignment for Deep
Learning Based Block-Online Blind Source Separation.” ICASSP 2018, Calgary,
Canada, 2018.
short: 'L. Drude, Takuya Higuchi, K. Kinoshita, T. Nakatani, R. Haeb-Umbach, in:
ICASSP 2018, Calgary, Canada, 2018.'
date_created: 2019-07-30T14:42:15Z
date_updated: 2022-01-06T06:51:24Z
department:
- _id: '54'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://groups.uni-paderborn.de/nt/pubs/2018/ICASSP_2018_Drude_Paper.pdf
oa: '1'
publication: ICASSP 2018, Calgary, Canada
related_material:
link:
- description: Poster
relation: supplementary_material
url: https://groups.uni-paderborn.de/nt/pubs/2018/ICASSP_2018_Drude_Poster.pdf
status: public
title: Dual Frequency- and Block-Permutation Alignment for Deep Learning Based Block-Online
Blind Source Separation
type: conference
user_id: '44006'
year: '2018'
...
---
_id: '12901'
abstract:
- lang: eng
text: This work examines acoustic beamformers employing neural networks (NNs) for
mask prediction as front-end for automatic speech recognition (ASR) systems for
practical scenarios like voice-enabled home devices. To test the versatility of
the mask predicting network, the system is evaluated with different recording
hardware, different microphone array designs, and different acoustic models of
the downstream ASR system. Significant gains in recognition accuracy are obtained
in all configurations despite the fact that the NN had been trained on mismatched
data. Unlike previous work, the NN is trained on a feature level objective, which
gives some performance advantage over a mask related criterion. Furthermore, different
approaches for realizing online, or adaptive, NN-based beamforming are explored,
where the online algorithms still show significant gains compared to the baseline
performance.
author:
- first_name: Christoph
full_name: Boeddeker, Christoph
id: '40767'
last_name: Boeddeker
- first_name: Hakan
full_name: Erdogan, Hakan
last_name: Erdogan
- first_name: Takuya
full_name: Yoshioka, Takuya
last_name: Yoshioka
- first_name: Reinhold
full_name: Haeb-Umbach, Reinhold
id: '242'
last_name: Haeb-Umbach
citation:
ama: 'Boeddeker C, Erdogan H, Yoshioka T, Haeb-Umbach R. Exploring Practical Aspects
of Neural Mask-Based Beamforming for Far-Field Speech Recognition. In: ICASSP
2018, Calgary, Canada. ; 2018.'
apa: Boeddeker, C., Erdogan, H., Yoshioka, T., & Haeb-Umbach, R. (2018). Exploring
Practical Aspects of Neural Mask-Based Beamforming for Far-Field Speech Recognition.
In ICASSP 2018, Calgary, Canada.
bibtex: '@inproceedings{Boeddeker_Erdogan_Yoshioka_Haeb-Umbach_2018, title={Exploring
Practical Aspects of Neural Mask-Based Beamforming for Far-Field Speech Recognition},
booktitle={ICASSP 2018, Calgary, Canada}, author={Boeddeker, Christoph and Erdogan,
Hakan and Yoshioka, Takuya and Haeb-Umbach, Reinhold}, year={2018} }'
chicago: Boeddeker, Christoph, Hakan Erdogan, Takuya Yoshioka, and Reinhold Haeb-Umbach.
“Exploring Practical Aspects of Neural Mask-Based Beamforming for Far-Field Speech
Recognition.” In ICASSP 2018, Calgary, Canada, 2018.
ieee: C. Boeddeker, H. Erdogan, T. Yoshioka, and R. Haeb-Umbach, “Exploring Practical
Aspects of Neural Mask-Based Beamforming for Far-Field Speech Recognition,” in
ICASSP 2018, Calgary, Canada, 2018.
mla: Boeddeker, Christoph, et al. “Exploring Practical Aspects of Neural Mask-Based
Beamforming for Far-Field Speech Recognition.” ICASSP 2018, Calgary, Canada,
2018.
short: 'C. Boeddeker, H. Erdogan, T. Yoshioka, R. Haeb-Umbach, in: ICASSP 2018,
Calgary, Canada, 2018.'
date_created: 2019-07-30T14:53:58Z
date_updated: 2022-01-06T06:51:24Z
department:
- _id: '54'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://groups.uni-paderborn.de/nt/pubs/2018/ICASSP_2018_Boeddeker_Paper.pdf
oa: '1'
publication: ICASSP 2018, Calgary, Canada
related_material:
link:
- description: Poster
relation: supplementary_material
url: https://groups.uni-paderborn.de/nt/pubs/2018/ICASSP_2018_Boeddeker_Slides.pdf
status: public
title: Exploring Practical Aspects of Neural Mask-Based Beamforming for Far-Field
Speech Recognition
type: conference
user_id: '44006'
year: '2018'
...
---
_id: '12899'
abstract:
- lang: eng
text: This contribution presents a speech enhancement system for the CHiME-5 Dinner
Party Scenario. The front-end employs multi-channel linear time-variant filtering
and achieves its gains without the use of a neural network. We present an adaptation
of blind source separation techniques to the CHiME-5 database which we call Guided
Source Separation (GSS). Using the baseline acoustic and language model, the combination
of Weighted Prediction Error based dereverberation, guided source separation,
and beamforming reduces the WER by 10:54% (relative) for the single array track
and by 21:12% (relative) on the multiple array track.
author:
- first_name: Christoph
full_name: Boeddeker, Christoph
id: '40767'
last_name: Boeddeker
- first_name: Jens
full_name: Heitkaemper, Jens
id: '27643'
last_name: Heitkaemper
- first_name: Joerg
full_name: Schmalenstroeer, Joerg
id: '460'
last_name: Schmalenstroeer
- first_name: Lukas
full_name: Drude, Lukas
id: '11213'
last_name: Drude
- first_name: Jahn
full_name: Heymann, Jahn
last_name: Heymann
- first_name: Reinhold
full_name: Haeb-Umbach, Reinhold
id: '242'
last_name: Haeb-Umbach
citation:
ama: 'Boeddeker C, Heitkaemper J, Schmalenstroeer J, Drude L, Heymann J, Haeb-Umbach
R. Front-End Processing for the CHiME-5 Dinner Party Scenario. In: Proc. CHiME
2018 Workshop on Speech Processing in Everyday Environments, Hyderabad, India.
; 2018.'
apa: Boeddeker, C., Heitkaemper, J., Schmalenstroeer, J., Drude, L., Heymann, J.,
& Haeb-Umbach, R. (2018). Front-End Processing for the CHiME-5 Dinner Party
Scenario. Proc. CHiME 2018 Workshop on Speech Processing in Everyday Environments,
Hyderabad, India.
bibtex: '@inproceedings{Boeddeker_Heitkaemper_Schmalenstroeer_Drude_Heymann_Haeb-Umbach_2018,
title={Front-End Processing for the CHiME-5 Dinner Party Scenario}, booktitle={Proc.
CHiME 2018 Workshop on Speech Processing in Everyday Environments, Hyderabad,
India}, author={Boeddeker, Christoph and Heitkaemper, Jens and Schmalenstroeer,
Joerg and Drude, Lukas and Heymann, Jahn and Haeb-Umbach, Reinhold}, year={2018}
}'
chicago: Boeddeker, Christoph, Jens Heitkaemper, Joerg Schmalenstroeer, Lukas Drude,
Jahn Heymann, and Reinhold Haeb-Umbach. “Front-End Processing for the CHiME-5
Dinner Party Scenario.” In Proc. CHiME 2018 Workshop on Speech Processing in
Everyday Environments, Hyderabad, India, 2018.
ieee: C. Boeddeker, J. Heitkaemper, J. Schmalenstroeer, L. Drude, J. Heymann, and
R. Haeb-Umbach, “Front-End Processing for the CHiME-5 Dinner Party Scenario,”
2018.
mla: Boeddeker, Christoph, et al. “Front-End Processing for the CHiME-5 Dinner Party
Scenario.” Proc. CHiME 2018 Workshop on Speech Processing in Everyday Environments,
Hyderabad, India, 2018.
short: 'C. Boeddeker, J. Heitkaemper, J. Schmalenstroeer, L. Drude, J. Heymann,
R. Haeb-Umbach, in: Proc. CHiME 2018 Workshop on Speech Processing in Everyday
Environments, Hyderabad, India, 2018.'
date_created: 2019-07-30T14:35:15Z
date_updated: 2023-10-26T08:14:15Z
department:
- _id: '54'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://groups.uni-paderborn.de/nt/pubs/2018/INTERSPEECH_2018_Heitkaemper_Paper.pdf
oa: '1'
project:
- _id: '52'
name: Computing Resources Provided by the Paderborn Center for Parallel Computing
publication: Proc. CHiME 2018 Workshop on Speech Processing in Everyday Environments,
Hyderabad, India
quality_controlled: '1'
related_material:
link:
- description: Poster
relation: supplementary_material
url: https://groups.uni-paderborn.de/nt/pubs/2018/INTERSPEECH_2018_Heitkaemper_Poster.pdf
status: public
title: Front-End Processing for the CHiME-5 Dinner Party Scenario
type: conference
user_id: '460'
year: '2018'
...
---
_id: '6859'
abstract:
- lang: eng
text: "Signal processing in WASNs is based on a software framework for hosting the
algorithms as well as on a set of wireless connected devices representing the
hardware. Each of the nodes contributes memory, processing power, communication
bandwidth and some sensor information for the tasks to be solved on the network.
\r\nIn this paper we present our MARVELO framework for distributed signal processing.
It is intended for transforming existing centralized implementations into distributed
versions. To this end, the software only needs a block-oriented implementation,
which MARVELO picks-up and distributes on the network. Additionally, our sensor
node hardware and the audio interfaces responsible for multi-channel recordings
are presented."
author:
- first_name: Haitham
full_name: Afifi, Haitham
id: '65718'
last_name: Afifi
- first_name: Joerg
full_name: Schmalenstroeer, Joerg
id: '460'
last_name: Schmalenstroeer
- first_name: Joerg
full_name: Ullmann, Joerg
id: '16256'
last_name: Ullmann
- first_name: Reinhold
full_name: Haeb-Umbach, Reinhold
id: '242'
last_name: Haeb-Umbach
- first_name: Holger
full_name: Karl, Holger
id: '126'
last_name: Karl
citation:
ama: 'Afifi H, Schmalenstroeer J, Ullmann J, Haeb-Umbach R, Karl H. MARVELO - A
Framework for Signal Processing in Wireless Acoustic Sensor Networks. In: Speech
Communication; 13th ITG-Symposium. ; 2018:1-5.'
apa: Afifi, H., Schmalenstroeer, J., Ullmann, J., Haeb-Umbach, R., & Karl, H.
(2018). MARVELO - A Framework for Signal Processing in Wireless Acoustic Sensor
Networks. Speech Communication; 13th ITG-Symposium, 1–5.
bibtex: '@inproceedings{Afifi_Schmalenstroeer_Ullmann_Haeb-Umbach_Karl_2018, title={MARVELO
- A Framework for Signal Processing in Wireless Acoustic Sensor Networks}, booktitle={Speech
Communication; 13th ITG-Symposium}, author={Afifi, Haitham and Schmalenstroeer,
Joerg and Ullmann, Joerg and Haeb-Umbach, Reinhold and Karl, Holger}, year={2018},
pages={1–5} }'
chicago: Afifi, Haitham, Joerg Schmalenstroeer, Joerg Ullmann, Reinhold Haeb-Umbach,
and Holger Karl. “MARVELO - A Framework for Signal Processing in Wireless Acoustic
Sensor Networks.” In Speech Communication; 13th ITG-Symposium, 1–5, 2018.
ieee: H. Afifi, J. Schmalenstroeer, J. Ullmann, R. Haeb-Umbach, and H. Karl, “MARVELO
- A Framework for Signal Processing in Wireless Acoustic Sensor Networks,” in
Speech Communication; 13th ITG-Symposium, 2018, pp. 1–5.
mla: Afifi, Haitham, et al. “MARVELO - A Framework for Signal Processing in Wireless
Acoustic Sensor Networks.” Speech Communication; 13th ITG-Symposium, 2018,
pp. 1–5.
short: 'H. Afifi, J. Schmalenstroeer, J. Ullmann, R. Haeb-Umbach, H. Karl, in: Speech
Communication; 13th ITG-Symposium, 2018, pp. 1–5.'
date_created: 2019-01-17T15:47:35Z
date_updated: 2023-10-26T08:15:32Z
department:
- _id: '75'
- _id: '54'
language:
- iso: eng
page: 1-5
project:
- _id: '27'
name: 'Akustische Sensornetzwerke - Teilprojekt '
- _id: '27'
name: Akustische Sensornetzwerke - Teilprojekt "Verteilte akustische Signalverarbeitung
über funkbasierte Sensornetzwerke
publication: Speech Communication; 13th ITG-Symposium
quality_controlled: '1'
status: public
title: MARVELO - A Framework for Signal Processing in Wireless Acoustic Sensor Networks
type: conference
user_id: '460'
year: '2018'
...
---
_id: '11747'
abstract:
- lang: eng
text: In this paper, we present a neural network based classification algorithm
for the discrimination of moving from stationary targets in the sight of an automotive
radar sensor. Compared to existing algorithms, the proposed algorithm can take
into account multiple local radar targets instead of performing classification
inference on each target individually resulting in superior discrimination accuracy,
especially suitable for non rigid objects, like pedestrians, which in general
have a wide velocity spread when multiple targets are detected.
author:
- first_name: Christopher
full_name: Grimm, Christopher
last_name: Grimm
- first_name: Tobias
full_name: Breddermann, Tobias
last_name: Breddermann
- first_name: Ridha
full_name: Farhoud, Ridha
last_name: Farhoud
- first_name: Tai
full_name: Fei, Tai
last_name: Fei
- first_name: Ernst
full_name: Warsitz, Ernst
last_name: Warsitz
- first_name: Reinhold
full_name: Haeb-Umbach, Reinhold
id: '242'
last_name: Haeb-Umbach
citation:
ama: 'Grimm C, Breddermann T, Farhoud R, Fei T, Warsitz E, Haeb-Umbach R. Discrimination
of Stationary from Moving Targets with Recurrent Neural Networks in Automotive
Radar. In: International Conference on Microwaves for Intelligent Mobility
(ICMIM) 2018. ; 2018.'
apa: Grimm, C., Breddermann, T., Farhoud, R., Fei, T., Warsitz, E., & Haeb-Umbach,
R. (2018). Discrimination of Stationary from Moving Targets with Recurrent Neural
Networks in Automotive Radar. International Conference on Microwaves for Intelligent
Mobility (ICMIM) 2018.
bibtex: '@inproceedings{Grimm_Breddermann_Farhoud_Fei_Warsitz_Haeb-Umbach_2018,
title={Discrimination of Stationary from Moving Targets with Recurrent Neural
Networks in Automotive Radar}, booktitle={International Conference on Microwaves
for Intelligent Mobility (ICMIM) 2018}, author={Grimm, Christopher and Breddermann,
Tobias and Farhoud, Ridha and Fei, Tai and Warsitz, Ernst and Haeb-Umbach, Reinhold},
year={2018} }'
chicago: Grimm, Christopher, Tobias Breddermann, Ridha Farhoud, Tai Fei, Ernst Warsitz,
and Reinhold Haeb-Umbach. “Discrimination of Stationary from Moving Targets with
Recurrent Neural Networks in Automotive Radar.” In International Conference
on Microwaves for Intelligent Mobility (ICMIM) 2018, 2018.
ieee: C. Grimm, T. Breddermann, R. Farhoud, T. Fei, E. Warsitz, and R. Haeb-Umbach,
“Discrimination of Stationary from Moving Targets with Recurrent Neural Networks
in Automotive Radar,” 2018.
mla: Grimm, Christopher, et al. “Discrimination of Stationary from Moving Targets
with Recurrent Neural Networks in Automotive Radar.” International Conference
on Microwaves for Intelligent Mobility (ICMIM) 2018, 2018.
short: 'C. Grimm, T. Breddermann, R. Farhoud, T. Fei, E. Warsitz, R. Haeb-Umbach,
in: International Conference on Microwaves for Intelligent Mobility (ICMIM) 2018,
2018.'
date_created: 2019-07-12T05:27:29Z
date_updated: 2023-11-20T16:37:39Z
department:
- _id: '54'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://groups.uni-paderborn.de/nt/pubs/2018/ICMIM_2018_Haeb-Umbach_Paper.pdf
oa: '1'
publication: International Conference on Microwaves for Intelligent Mobility (ICMIM)
2018
quality_controlled: '1'
status: public
title: Discrimination of Stationary from Moving Targets with Recurrent Neural Networks
in Automotive Radar
type: conference
user_id: '242'
year: '2018'
...
---
_id: '11907'
abstract:
- lang: eng
text: The invention of the Variational Autoencoder enables the application of Neural
Networks to a wide range of tasks in unsupervised learning, including the field
of Acoustic Unit Discovery (AUD). The recently proposed Hidden Markov Model Variational
Autoencoder (HMMVAE) allows a joint training of a neural network based feature
extractor and a structured prior for the latent space given by a Hidden Markov
Model. It has been shown that the HMMVAE significantly outperforms pure GMM-HMM
based systems on the AUD task. However, the HMMVAE cannot autonomously infer the
number of acoustic units and thus relies on the GMM-HMM system for initialization.
This paper introduces the Bayesian Hidden Markov Model Variational Autoencoder
(BHMMVAE) which solves these issues by embedding the HMMVAE in a Bayesian framework
with a Dirichlet Process Prior for the distribution of the acoustic units, and
diagonal or full-covariance Gaussians as emission distributions. Experiments on
TIMIT and Xitsonga show that the BHMMVAE is able to autonomously infer a reasonable
number of acoustic units, can be initialized without supervision by a GMM-HMM
system, achieves computationally efficient stochastic variational inference by
using natural gradient descent, and, additionally, improves the AUD performance
over the HMMVAE.
author:
- first_name: Thomas
full_name: Glarner, Thomas
id: '14169'
last_name: Glarner
- first_name: Patrick
full_name: Hanebrink, Patrick
last_name: Hanebrink
- first_name: Janek
full_name: Ebbers, Janek
id: '34851'
last_name: Ebbers
- first_name: Reinhold
full_name: Haeb-Umbach, Reinhold
id: '242'
last_name: Haeb-Umbach
citation:
ama: 'Glarner T, Hanebrink P, Ebbers J, Haeb-Umbach R. Full Bayesian Hidden Markov
Model Variational Autoencoder for Acoustic Unit Discovery. In: INTERSPEECH
2018, Hyderabad, India. ; 2018.'
apa: Glarner, T., Hanebrink, P., Ebbers, J., & Haeb-Umbach, R. (2018). Full
Bayesian Hidden Markov Model Variational Autoencoder for Acoustic Unit Discovery.
INTERSPEECH 2018, Hyderabad, India.
bibtex: '@inproceedings{Glarner_Hanebrink_Ebbers_Haeb-Umbach_2018, title={Full Bayesian
Hidden Markov Model Variational Autoencoder for Acoustic Unit Discovery}, booktitle={INTERSPEECH
2018, Hyderabad, India}, author={Glarner, Thomas and Hanebrink, Patrick and Ebbers,
Janek and Haeb-Umbach, Reinhold}, year={2018} }'
chicago: Glarner, Thomas, Patrick Hanebrink, Janek Ebbers, and Reinhold Haeb-Umbach.
“Full Bayesian Hidden Markov Model Variational Autoencoder for Acoustic Unit Discovery.”
In INTERSPEECH 2018, Hyderabad, India, 2018.
ieee: T. Glarner, P. Hanebrink, J. Ebbers, and R. Haeb-Umbach, “Full Bayesian Hidden
Markov Model Variational Autoencoder for Acoustic Unit Discovery,” 2018.
mla: Glarner, Thomas, et al. “Full Bayesian Hidden Markov Model Variational Autoencoder
for Acoustic Unit Discovery.” INTERSPEECH 2018, Hyderabad, India, 2018.
short: 'T. Glarner, P. Hanebrink, J. Ebbers, R. Haeb-Umbach, in: INTERSPEECH 2018,
Hyderabad, India, 2018.'
date_created: 2019-07-12T05:30:34Z
date_updated: 2023-11-22T08:29:22Z
department:
- _id: '54'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://groups.uni-paderborn.de/nt/pubs/2018/INTERSPEECH_2018_Glarner_Paper.pdf
oa: '1'
publication: INTERSPEECH 2018, Hyderabad, India
quality_controlled: '1'
related_material:
link:
- description: Slides
relation: supplementary_material
url: https://groups.uni-paderborn.de/nt/pubs/2018/INTERSPEECH_2018_Glarner_Slides.pdf
status: public
title: Full Bayesian Hidden Markov Model Variational Autoencoder for Acoustic Unit
Discovery
type: conference
user_id: '34851'
year: '2018'
...