---
_id: '20753'
abstract:
- lang: eng
text: 'In this paper we present our system for the detection and classification
of acoustic scenes and events (DCASE) 2020 Challenge Task 4: Sound event detection
and separation in domestic environments. We introduce two new models: the forward-backward
convolutional recurrent neural network (FBCRNN) and the tag-conditioned convolutional
neural network (CNN). The FBCRNN employs two recurrent neural network (RNN) classifiers
sharing the same CNN for preprocessing. With one RNN processing a recording in
forward direction and the other in backward direction, the two networks are trained
to jointly predict audio tags, i.e., weak labels, at each time step within a recording,
given that at each time step they have jointly processed the whole recording.
The proposed training encourages the classifiers to tag events as soon as possible.
Therefore, after training, the networks can be applied to shorter audio segments
of, e.g., 200ms, allowing sound event detection (SED). Further, we propose a tag-conditioned
CNN to complement SED. It is trained to predict strong labels while using (predicted)
tags, i.e., weak labels, as additional input. For training pseudo strong labels
from a FBCRNN ensemble are used. The presented system scored the fourth and third
place in the systems and teams rankings, respectively. Subsequent improvements
allow our system to even outperform the challenge baseline and winner systems
in average by, respectively, 18.0% and 2.2% event-based F1-score on the validation
set. Source code is publicly available at https://github.com/fgnt/pb_sed.'
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. Forward-Backward Convolutional Recurrent Neural Networks
and Tag-Conditioned Convolutional Neural Networks for Weakly Labeled Semi-Supervised
Sound Event Detection. In: Proceedings of the Detection and Classification
of Acoustic Scenes and Events 2020 Workshop (DCASE2020). ; 2020.'
apa: Ebbers, J., & Haeb-Umbach, R. (2020). Forward-Backward Convolutional Recurrent
Neural Networks and Tag-Conditioned Convolutional Neural Networks for Weakly Labeled
Semi-Supervised Sound Event Detection. Proceedings of the Detection and Classification
of Acoustic Scenes and Events 2020 Workshop (DCASE2020).
bibtex: '@inproceedings{Ebbers_Haeb-Umbach_2020, title={Forward-Backward Convolutional
Recurrent Neural Networks and Tag-Conditioned Convolutional Neural Networks for
Weakly Labeled Semi-Supervised Sound Event Detection}, booktitle={Proceedings
of the Detection and Classification of Acoustic Scenes and Events 2020 Workshop
(DCASE2020)}, author={Ebbers, Janek and Haeb-Umbach, Reinhold}, year={2020} }'
chicago: Ebbers, Janek, and Reinhold Haeb-Umbach. “Forward-Backward Convolutional
Recurrent Neural Networks and Tag-Conditioned Convolutional Neural Networks for
Weakly Labeled Semi-Supervised Sound Event Detection.” In Proceedings of the
Detection and Classification of Acoustic Scenes and Events 2020 Workshop (DCASE2020),
2020.
ieee: J. Ebbers and R. Haeb-Umbach, “Forward-Backward Convolutional Recurrent Neural
Networks and Tag-Conditioned Convolutional Neural Networks for Weakly Labeled
Semi-Supervised Sound Event Detection,” 2020.
mla: Ebbers, Janek, and Reinhold Haeb-Umbach. “Forward-Backward Convolutional Recurrent
Neural Networks and Tag-Conditioned Convolutional Neural Networks for Weakly Labeled
Semi-Supervised Sound Event Detection.” Proceedings of the Detection and Classification
of Acoustic Scenes and Events 2020 Workshop (DCASE2020), 2020.
short: 'J. Ebbers, R. Haeb-Umbach, in: Proceedings of the Detection and Classification
of Acoustic Scenes and Events 2020 Workshop (DCASE2020), 2020.'
date_created: 2020-12-16T08:55:27Z
date_updated: 2023-11-22T08:27:32Z
ddc:
- '000'
department:
- _id: '54'
file:
- access_level: open_access
content_type: application/pdf
creator: huesera
date_created: 2020-12-16T08:57:22Z
date_updated: 2020-12-16T08:57:22Z
file_id: '20754'
file_name: DCASE2020Workshop_Ebbers_Paper.pdf
file_size: 108326
relation: main_file
file_date_updated: 2020-12-16T08:57:22Z
has_accepted_license: '1'
language:
- iso: eng
oa: '1'
project:
- _id: '52'
name: 'PC2: Computing Resources Provided by the Paderborn Center for Parallel Computing'
publication: Proceedings of the Detection and Classification of Acoustic Scenes and
Events 2020 Workshop (DCASE2020)
quality_controlled: '1'
status: public
title: Forward-Backward Convolutional Recurrent Neural Networks and Tag-Conditioned
Convolutional Neural Networks for Weakly Labeled Semi-Supervised Sound Event Detection
type: conference
user_id: '34851'
year: '2020'
...
---
_id: '20695'
author:
- first_name: Christoph
full_name: Boeddeker, Christoph
id: '40767'
last_name: Boeddeker
- first_name: Tomohiro
full_name: Nakatani, Tomohiro
last_name: Nakatani
- first_name: Keisuke
full_name: Kinoshita, Keisuke
last_name: Kinoshita
- first_name: Reinhold
full_name: Haeb-Umbach, Reinhold
id: '242'
last_name: Haeb-Umbach
citation:
ama: 'Boeddeker C, Nakatani T, Kinoshita K, Haeb-Umbach R. Jointly Optimal Dereverberation
and Beamforming. In: ICASSP 2020 - 2020 IEEE International Conference on Acoustics,
Speech and Signal Processing (ICASSP). ; 2020. doi:10.1109/icassp40776.2020.9054393'
apa: Boeddeker, C., Nakatani, T., Kinoshita, K., & Haeb-Umbach, R. (2020). Jointly
Optimal Dereverberation and Beamforming. ICASSP 2020 - 2020 IEEE International
Conference on Acoustics, Speech and Signal Processing (ICASSP). https://doi.org/10.1109/icassp40776.2020.9054393
bibtex: '@inproceedings{Boeddeker_Nakatani_Kinoshita_Haeb-Umbach_2020, title={Jointly
Optimal Dereverberation and Beamforming}, DOI={10.1109/icassp40776.2020.9054393},
booktitle={ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech
and Signal Processing (ICASSP)}, author={Boeddeker, Christoph and Nakatani, Tomohiro
and Kinoshita, Keisuke and Haeb-Umbach, Reinhold}, year={2020} }'
chicago: Boeddeker, Christoph, Tomohiro Nakatani, Keisuke Kinoshita, and Reinhold
Haeb-Umbach. “Jointly Optimal Dereverberation and Beamforming.” In ICASSP 2020
- 2020 IEEE International Conference on Acoustics, Speech and Signal Processing
(ICASSP), 2020. https://doi.org/10.1109/icassp40776.2020.9054393.
ieee: 'C. Boeddeker, T. Nakatani, K. Kinoshita, and R. Haeb-Umbach, “Jointly Optimal
Dereverberation and Beamforming,” 2020, doi: 10.1109/icassp40776.2020.9054393.'
mla: Boeddeker, Christoph, et al. “Jointly Optimal Dereverberation and Beamforming.”
ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal
Processing (ICASSP), 2020, doi:10.1109/icassp40776.2020.9054393.
short: 'C. Boeddeker, T. Nakatani, K. Kinoshita, R. Haeb-Umbach, in: ICASSP 2020
- 2020 IEEE International Conference on Acoustics, Speech and Signal Processing
(ICASSP), 2020.'
date_created: 2020-12-11T12:28:49Z
date_updated: 2024-11-14T09:17:32Z
ddc:
- '000'
department:
- _id: '54'
doi: 10.1109/icassp40776.2020.9054393
file:
- access_level: open_access
content_type: application/pdf
creator: cbj
date_created: 2020-12-11T12:32:44Z
date_updated: 2020-12-11T12:32:44Z
file_id: '20698'
file_name: convBF.pdf
file_size: 200127
relation: main_file
file_date_updated: 2020-12-11T12:32:44Z
has_accepted_license: '1'
language:
- iso: eng
oa: '1'
project:
- _id: '52'
name: Computing Resources Provided by the Paderborn Center for Parallel Computing
publication: ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech
and Signal Processing (ICASSP)
publication_identifier:
isbn:
- '9781509066315'
publication_status: published
status: public
title: Jointly Optimal Dereverberation and Beamforming
type: conference
user_id: '40767'
year: '2020'
...
---
_id: '17762'
abstract:
- lang: eng
text: 'Abstract 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?! forschung. 2019;44(1):12-15.
doi:10.1002/fors.201970104
apa: Haeb-Umbach, R. (2019). Lektionen für Alexa \& Co?! Forschung, 44(1),
12–15. https://doi.org/10.1002/fors.201970104
bibtex: '@article{Haeb-Umbach_2019, title={Lektionen für Alexa \& Co?!}, volume={44},
DOI={10.1002/fors.201970104},
number={1}, journal={forschung}, author={Haeb-Umbach, Reinhold}, year={2019},
pages={12–15} }'
chicago: 'Haeb-Umbach, Reinhold. “Lektionen Für Alexa \& Co?!” Forschung
44, no. 1 (2019): 12–15. https://doi.org/10.1002/fors.201970104.'
ieee: R. Haeb-Umbach, “Lektionen für Alexa \& Co?!,” forschung, vol.
44, no. 1, pp. 12–15, 2019.
mla: Haeb-Umbach, Reinhold. “Lektionen Für Alexa \& Co?!” Forschung,
vol. 44, no. 1, 2019, pp. 12–15, doi:10.1002/fors.201970104.
short: R. Haeb-Umbach, Forschung 44 (2019) 12–15.
date_created: 2020-08-10T09:51:09Z
date_updated: 2022-01-06T06:53:19Z
department:
- _id: '54'
doi: 10.1002/fors.201970104
intvolume: ' 44'
issue: '1'
language:
- iso: eng
page: 12-15
publication: forschung
status: public
title: Lektionen für Alexa \& Co?!
type: journal_article
user_id: '44006'
volume: 44
year: '2019'
...
---
_id: '19446'
abstract:
- lang: eng
text: 'We present a multi-channel database of overlapping speech for training, evaluation,
and detailed analysis of source separation and extraction algorithms: SMS-WSJ
-- Spatialized Multi-Speaker Wall Street Journal. It consists of artificially
mixed speech taken from the WSJ database, but unlike earlier databases we consider
all WSJ0+1 utterances and take care of strictly separating the speaker sets present
in the training, validation and test sets. When spatializing the data we ensure
a high degree of randomness w.r.t. room size, array center and rotation, as well
as speaker position. Furthermore, this paper offers a critical assessment of recently
proposed measures of source separation performance. Alongside the code to generate
the database we provide a source separation baseline and a Kaldi recipe with competitive
word error rates to provide common ground for evaluation.'
author:
- first_name: Lukas
full_name: Drude, Lukas
last_name: Drude
- first_name: Jens
full_name: Heitkaemper, Jens
id: '27643'
last_name: Heitkaemper
- 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, Heitkaemper J, Boeddeker C, Haeb-Umbach R. SMS-WSJ: Database, performance
measures, and baseline recipe for multi-channel source separation and recognition.
ArXiv e-prints. 2019.'
apa: 'Drude, L., Heitkaemper, J., Boeddeker, C., & Haeb-Umbach, R. (2019). SMS-WSJ:
Database, performance measures, and baseline recipe for multi-channel source separation
and recognition. ArXiv E-Prints.'
bibtex: '@article{Drude_Heitkaemper_Boeddeker_Haeb-Umbach_2019, title={SMS-WSJ:
Database, performance measures, and baseline recipe for multi-channel source separation
and recognition}, journal={ArXiv e-prints}, author={Drude, Lukas and Heitkaemper,
Jens and Boeddeker, Christoph and Haeb-Umbach, Reinhold}, year={2019} }'
chicago: 'Drude, Lukas, Jens Heitkaemper, Christoph Boeddeker, and Reinhold Haeb-Umbach.
“SMS-WSJ: Database, Performance Measures, and Baseline Recipe for Multi-Channel
Source Separation and Recognition.” ArXiv E-Prints, 2019.'
ieee: 'L. Drude, J. Heitkaemper, C. Boeddeker, and R. Haeb-Umbach, “SMS-WSJ: Database,
performance measures, and baseline recipe for multi-channel source separation
and recognition,” ArXiv e-prints, 2019.'
mla: 'Drude, Lukas, et al. “SMS-WSJ: Database, Performance Measures, and Baseline
Recipe for Multi-Channel Source Separation and Recognition.” ArXiv E-Prints,
2019.'
short: L. Drude, J. Heitkaemper, C. Boeddeker, R. Haeb-Umbach, ArXiv E-Prints (2019).
date_created: 2020-09-16T07:59:46Z
date_updated: 2022-01-06T06:54:04Z
ddc:
- '000'
department:
- _id: '54'
file:
- access_level: open_access
content_type: application/pdf
creator: huesera
date_created: 2020-09-16T08:00:56Z
date_updated: 2020-12-11T12:22:31Z
file_id: '19448'
file_name: ArXiv_2019_Drude.pdf
file_size: 288594
relation: main_file
file_date_updated: 2020-12-11T12:22:31Z
has_accepted_license: '1'
language:
- iso: eng
oa: '1'
project:
- _id: '52'
name: Computing Resources Provided by the Paderborn Center for Parallel Computing
publication: ArXiv e-prints
status: public
title: 'SMS-WSJ: Database, performance measures, and baseline recipe for multi-channel
source separation and recognition'
type: journal_article
user_id: '40767'
year: '2019'
...
---
_id: '11965'
abstract:
- lang: eng
text: 'We present an unsupervised training approach for a neural network-based mask
estimator in an acoustic beamforming application. The network is trained to maximize
a likelihood criterion derived from a spatial mixture model of the observations.
It is trained from scratch without requiring any parallel data consisting of degraded
input and clean training targets. Thus, training can be carried out on real recordings
of noisy speech rather than simulated ones. In contrast to previous work on unsupervised
training of neural mask estimators, our approach avoids the need for a possibly
pre-trained teacher model entirely. We demonstrate the effectiveness of our approach
by speech recognition experiments on two different datasets: one mainly deteriorated
by noise (CHiME 4) and one by reverberation (REVERB). The results show that the
performance of the proposed system is on par with a supervised system using oracle
target masks for training and with a system trained using a model-based teacher.'
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: Reinhold
full_name: Haeb-Umbach, Reinhold
id: '242'
last_name: Haeb-Umbach
citation:
ama: 'Drude L, Heymann J, Haeb-Umbach R. Unsupervised training of neural mask-based
beamforming. In: INTERSPEECH 2019, Graz, Austria. ; 2019.'
apa: Drude, L., Heymann, J., & Haeb-Umbach, R. (2019). Unsupervised training
of neural mask-based beamforming. In INTERSPEECH 2019, Graz, Austria.
bibtex: '@inproceedings{Drude_Heymann_Haeb-Umbach_2019, title={Unsupervised training
of neural mask-based beamforming}, booktitle={INTERSPEECH 2019, Graz, Austria},
author={Drude, Lukas and Heymann, Jahn and Haeb-Umbach, Reinhold}, year={2019}
}'
chicago: Drude, Lukas, Jahn Heymann, and Reinhold Haeb-Umbach. “Unsupervised Training
of Neural Mask-Based Beamforming.” In INTERSPEECH 2019, Graz, Austria,
2019.
ieee: L. Drude, J. Heymann, and R. Haeb-Umbach, “Unsupervised training of neural
mask-based beamforming,” in INTERSPEECH 2019, Graz, Austria, 2019.
mla: Drude, Lukas, et al. “Unsupervised Training of Neural Mask-Based Beamforming.”
INTERSPEECH 2019, Graz, Austria, 2019.
short: 'L. Drude, J. Heymann, R. Haeb-Umbach, in: INTERSPEECH 2019, Graz, Austria,
2019.'
date_created: 2019-07-18T09:11:39Z
date_updated: 2022-01-06T06:51:14Z
ddc:
- '000'
department:
- _id: '54'
file:
- access_level: open_access
content_type: application/pdf
creator: huesera
date_created: 2019-08-13T06:36:44Z
date_updated: 2019-08-13T06:41:35Z
file_id: '12914'
file_name: INTERSPEECH_2019_Drude_Paper.pdf
file_size: 223413
relation: main_file
file_date_updated: 2019-08-13T06:41:35Z
has_accepted_license: '1'
language:
- iso: eng
oa: '1'
project:
- _id: '52'
name: Computing Resources Provided by the Paderborn Center for Parallel Computing
publication: INTERSPEECH 2019, Graz, Austria
status: public
title: Unsupervised training of neural mask-based beamforming
type: conference
user_id: '59789'
year: '2019'
...
---
_id: '12874'
abstract:
- lang: eng
text: We propose a training scheme to train neural network-based source separation
algorithms from scratch when parallel clean data is unavailable. In particular,
we demonstrate that an unsupervised spatial clustering algorithm is sufficient
to guide the training of a deep clustering system. We argue that previous work
on deep clustering requires strong supervision and elaborate on why this is a
limitation. We demonstrate that (a) the single-channel deep clustering system
trained according to the proposed scheme alone is able to achieve a similar performance
as the multi-channel teacher in terms of word error rates and (b) initializing
the spatial clustering approach with the deep clustering result yields a relative
word error rate reduction of 26% over the unsupervised teacher.
author:
- first_name: Lukas
full_name: Drude, Lukas
id: '11213'
last_name: Drude
- first_name: Daniel
full_name: Hasenklever, Daniel
last_name: Hasenklever
- first_name: Reinhold
full_name: Haeb-Umbach, Reinhold
id: '242'
last_name: Haeb-Umbach
citation:
ama: 'Drude L, Hasenklever D, Haeb-Umbach R. Unsupervised Training of a Deep Clustering
Model for Multichannel Blind Source Separation. In: ICASSP 2019, Brighton,
UK. ; 2019.'
apa: Drude, L., Hasenklever, D., & Haeb-Umbach, R. (2019). Unsupervised Training
of a Deep Clustering Model for Multichannel Blind Source Separation. In ICASSP
2019, Brighton, UK.
bibtex: '@inproceedings{Drude_Hasenklever_Haeb-Umbach_2019, title={Unsupervised
Training of a Deep Clustering Model for Multichannel Blind Source Separation},
booktitle={ICASSP 2019, Brighton, UK}, author={Drude, Lukas and Hasenklever, Daniel
and Haeb-Umbach, Reinhold}, year={2019} }'
chicago: Drude, Lukas, Daniel Hasenklever, and Reinhold Haeb-Umbach. “Unsupervised
Training of a Deep Clustering Model for Multichannel Blind Source Separation.”
In ICASSP 2019, Brighton, UK, 2019.
ieee: L. Drude, D. Hasenklever, and R. Haeb-Umbach, “Unsupervised Training of a
Deep Clustering Model for Multichannel Blind Source Separation,” in ICASSP
2019, Brighton, UK, 2019.
mla: Drude, Lukas, et al. “Unsupervised Training of a Deep Clustering Model for
Multichannel Blind Source Separation.” ICASSP 2019, Brighton, UK, 2019.
short: 'L. Drude, D. Hasenklever, R. Haeb-Umbach, in: ICASSP 2019, Brighton, UK,
2019.'
date_created: 2019-07-23T07:37:54Z
date_updated: 2022-01-06T06:51:21Z
ddc:
- '000'
department:
- _id: '54'
file:
- access_level: open_access
content_type: application/pdf
creator: huesera
date_created: 2019-08-14T07:19:13Z
date_updated: 2019-08-14T07:19:13Z
file_id: '12925'
file_name: ICASSP_2019_Drude_Paper.pdf
file_size: 368225
relation: main_file
file_date_updated: 2019-08-14T07:19:13Z
has_accepted_license: '1'
language:
- iso: eng
oa: '1'
project:
- _id: '52'
name: Computing Resources Provided by the Paderborn Center for Parallel Computing
publication: ICASSP 2019, Brighton, UK
status: public
title: Unsupervised Training of a Deep Clustering Model for Multichannel Blind Source
Separation
type: conference
user_id: '59789'
year: '2019'
...
---
_id: '12875'
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. First proposed as an iterative algorithm, follow-up works have reformulated
it as a recursive least squares algorithm and therefore enabled its use in online
applications. For this algorithm, the estimation of the power spectral density
(PSD) of the anechoic signal plays an important role and strongly influences its
performance. Recently, we showed that using a neural network PSD estimator leads
to improved performance for online automatic speech recognition. This, however,
comes at a price. To train the network, we require parallel data, i.e., utterances
simultaneously available in clean and reverberated form. Here we propose to overcome
this limitation by training the network jointly with the acoustic model of the
speech recognizer. To be specific, the gradients computed from the cross-entropy
loss between the target senone sequence and the acoustic model network output
is backpropagated through the complex-valued dereverberation filter estimation
to the neural network for PSD estimation. Evaluation on two databases demonstrates
improved performance for on-line processing scenarios while imposing fewer requirements
on the available training data and thus widening the range of applications.
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. Joint Optimization
of Neural Network-based WPE Dereverberation and Acoustic Model for Robust Online
ASR. In: ICASSP 2019, Brighton, UK. ; 2019.'
apa: Heymann, J., Drude, L., Haeb-Umbach, R., Kinoshita, K., & Nakatani, T.
(2019). Joint Optimization of Neural Network-based WPE Dereverberation and Acoustic
Model for Robust Online ASR. In ICASSP 2019, Brighton, UK.
bibtex: '@inproceedings{Heymann_Drude_Haeb-Umbach_Kinoshita_Nakatani_2019, title={Joint
Optimization of Neural Network-based WPE Dereverberation and Acoustic Model for
Robust Online ASR}, booktitle={ICASSP 2019, Brighton, UK}, author={Heymann, Jahn
and Drude, Lukas and Haeb-Umbach, Reinhold and Kinoshita, Keisuke and Nakatani,
Tomohiro}, year={2019} }'
chicago: Heymann, Jahn, Lukas Drude, Reinhold Haeb-Umbach, Keisuke Kinoshita, and
Tomohiro Nakatani. “Joint Optimization of Neural Network-Based WPE Dereverberation
and Acoustic Model for Robust Online ASR.” In ICASSP 2019, Brighton, UK,
2019.
ieee: J. Heymann, L. Drude, R. Haeb-Umbach, K. Kinoshita, and T. Nakatani, “Joint
Optimization of Neural Network-based WPE Dereverberation and Acoustic Model for
Robust Online ASR,” in ICASSP 2019, Brighton, UK, 2019.
mla: Heymann, Jahn, et al. “Joint Optimization of Neural Network-Based WPE Dereverberation
and Acoustic Model for Robust Online ASR.” ICASSP 2019, Brighton, UK, 2019.
short: 'J. Heymann, L. Drude, R. Haeb-Umbach, K. Kinoshita, T. Nakatani, in: ICASSP
2019, Brighton, UK, 2019.'
date_created: 2019-07-23T07:42:26Z
date_updated: 2022-01-06T06:51:22Z
ddc:
- '000'
department:
- _id: '54'
file:
- access_level: open_access
content_type: application/pdf
creator: huesera
date_created: 2019-12-17T07:28:06Z
date_updated: 2019-12-17T07:28:06Z
file_id: '15334'
file_name: ICASSP_2019_Heymann_Paper.pdf
file_size: 199109
relation: main_file
file_date_updated: 2019-12-17T07:28:06Z
has_accepted_license: '1'
language:
- iso: eng
oa: '1'
project:
- _id: '52'
name: Computing Resources Provided by the Paderborn Center for Parallel Computing
publication: ICASSP 2019, Brighton, UK
status: public
title: Joint Optimization of Neural Network-based WPE Dereverberation and Acoustic
Model for Robust Online ASR
type: conference
user_id: '59789'
year: '2019'
...
---
_id: '12876'
abstract:
- lang: eng
text: In this paper, we present libDirectional, a MATLAB library for directional
statistics and directional estimation. It supports a variety of commonly used
distributions on the unit circle, such as the von Mises, wrapped normal, and wrapped
Cauchy distributions. Furthermore, various distributions on higher-dimensional
manifolds such as the unit hypersphere and the hypertorus are available. Based
on these distributions, several recursive filtering algorithms in libDirectional
allow estimation on these manifolds. The functionality is implemented in a clear,
well-documented, and object-oriented structure that is both easy to use and easy
to extend.
author:
- first_name: Gerhard
full_name: Kurz, Gerhard
last_name: Kurz
- first_name: Igor
full_name: Gilitschenski, Igor
last_name: Gilitschenski
- first_name: Florian
full_name: Pfaff, Florian
last_name: Pfaff
- first_name: Lukas
full_name: Drude, Lukas
id: '11213'
last_name: Drude
- first_name: Uwe D.
full_name: Hanebeck, Uwe D.
last_name: Hanebeck
- first_name: Reinhold
full_name: Haeb-Umbach, Reinhold
id: '242'
last_name: Haeb-Umbach
- first_name: Roland Y.
full_name: Siegwart, Roland Y.
last_name: Siegwart
citation:
ama: 'Kurz G, Gilitschenski I, Pfaff F, et al. Directional Statistics and Filtering
Using libDirectional. In: Journal of Statistical Software 89(4). ; 2019.'
apa: Kurz, G., Gilitschenski, I., Pfaff, F., Drude, L., Hanebeck, U. D., Haeb-Umbach,
R., & Siegwart, R. Y. (2019). Directional Statistics and Filtering Using libDirectional.
In Journal of Statistical Software 89(4).
bibtex: '@inproceedings{Kurz_Gilitschenski_Pfaff_Drude_Hanebeck_Haeb-Umbach_Siegwart_2019,
title={Directional Statistics and Filtering Using libDirectional}, booktitle={Journal
of Statistical Software 89(4)}, author={Kurz, Gerhard and Gilitschenski, Igor
and Pfaff, Florian and Drude, Lukas and Hanebeck, Uwe D. and Haeb-Umbach, Reinhold
and Siegwart, Roland Y.}, year={2019} }'
chicago: Kurz, Gerhard, Igor Gilitschenski, Florian Pfaff, Lukas Drude, Uwe D. Hanebeck,
Reinhold Haeb-Umbach, and Roland Y. Siegwart. “Directional Statistics and Filtering
Using LibDirectional.” In Journal of Statistical Software 89(4), 2019.
ieee: G. Kurz et al., “Directional Statistics and Filtering Using libDirectional,”
in Journal of Statistical Software 89(4), 2019.
mla: Kurz, Gerhard, et al. “Directional Statistics and Filtering Using LibDirectional.”
Journal of Statistical Software 89(4), 2019.
short: 'G. Kurz, I. Gilitschenski, F. Pfaff, L. Drude, U.D. Hanebeck, R. Haeb-Umbach,
R.Y. Siegwart, in: Journal of Statistical Software 89(4), 2019.'
date_created: 2019-07-23T07:44:59Z
date_updated: 2022-01-06T06:51:22Z
ddc:
- '000'
department:
- _id: '54'
file:
- access_level: open_access
content_type: application/pdf
creator: huesera
date_created: 2019-08-14T07:16:05Z
date_updated: 2019-08-14T07:16:05Z
file_id: '12923'
file_name: JournalofStatisticalSoftware_2019_Drude_Paper.pdf
file_size: 1522964
relation: main_file
file_date_updated: 2019-08-14T07:16:05Z
has_accepted_license: '1'
language:
- iso: eng
oa: '1'
publication: Journal of Statistical Software 89(4)
status: public
title: Directional Statistics and Filtering Using libDirectional
type: conference
user_id: '59789'
year: '2019'
...
---
_id: '12890'
abstract:
- lang: eng
text: 'We formulate a generic framework for blind source separation (BSS), which
allows integrating data-driven spectro-temporal methods, such as deep clustering
and deep attractor networks, with physically motivated probabilistic spatial methods,
such as complex angular central Gaussian mixture models. The integrated model
exploits the complementary strengths of the two approaches to BSS: the strong
modeling power of neural networks, which, however, is based on supervised learning,
and the ease of unsupervised learning of the spatial mixture models whose few
parameters can be estimated on as little as a single segment of a real mixture
of speech. Experiments are carried out on both artificially mixed speech and true
recordings of speech mixtures. The experiments verify that the integrated models
consistently outperform the individual components. We further extend the models
to cope with noisy, reverberant speech and introduce a cross-domain teacher–student
training where the mixture model serves as the teacher to provide training targets
for the student neural network.'
author:
- 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
citation:
ama: Drude L, Haeb-Umbach R. Integration of Neural Networks and Probabilistic Spatial
Models for Acoustic Blind Source Separation. IEEE Journal of Selected Topics
in Signal Processing. 2019. doi:10.1109/JSTSP.2019.2912565
apa: Drude, L., & Haeb-Umbach, R. (2019). Integration of Neural Networks and
Probabilistic Spatial Models for Acoustic Blind Source Separation. IEEE Journal
of Selected Topics in Signal Processing. https://doi.org/10.1109/JSTSP.2019.2912565
bibtex: '@article{Drude_Haeb-Umbach_2019, title={Integration of Neural Networks
and Probabilistic Spatial Models for Acoustic Blind Source Separation}, DOI={10.1109/JSTSP.2019.2912565},
journal={IEEE Journal of Selected Topics in Signal Processing}, author={Drude,
Lukas and Haeb-Umbach, Reinhold}, year={2019} }'
chicago: Drude, Lukas, and Reinhold Haeb-Umbach. “Integration of Neural Networks
and Probabilistic Spatial Models for Acoustic Blind Source Separation.” IEEE
Journal of Selected Topics in Signal Processing, 2019. https://doi.org/10.1109/JSTSP.2019.2912565.
ieee: L. Drude and R. Haeb-Umbach, “Integration of Neural Networks and Probabilistic
Spatial Models for Acoustic Blind Source Separation,” IEEE Journal of Selected
Topics in Signal Processing, 2019.
mla: Drude, Lukas, and Reinhold Haeb-Umbach. “Integration of Neural Networks and
Probabilistic Spatial Models for Acoustic Blind Source Separation.” IEEE Journal
of Selected Topics in Signal Processing, 2019, doi:10.1109/JSTSP.2019.2912565.
short: L. Drude, R. Haeb-Umbach, IEEE Journal of Selected Topics in Signal Processing
(2019).
date_created: 2019-07-26T08:38:46Z
date_updated: 2022-01-06T06:51:23Z
ddc:
- '050'
department:
- _id: '54'
doi: 10.1109/JSTSP.2019.2912565
file:
- access_level: open_access
content_type: application/pdf
creator: huesera
date_created: 2019-08-07T07:12:21Z
date_updated: 2019-08-14T07:11:22Z
file_id: '12903'
file_name: IEEE Jounal_2019_Drude_Paper.pdf
file_size: 967424
relation: main_file
file_date_updated: 2019-08-14T07:11:22Z
has_accepted_license: '1'
language:
- iso: eng
oa: '1'
project:
- _id: '52'
name: Computing Resources Provided by the Paderborn Center for Parallel Computing
publication: IEEE Journal of Selected Topics in Signal Processing
publication_identifier:
eissn:
- 1941-0484
status: public
title: Integration of Neural Networks and Probabilistic Spatial Models for Acoustic
Blind Source Separation
type: journal_article
user_id: '11213'
year: '2019'
...
---
_id: '15812'
abstract:
- lang: eng
text: Connectionist temporal classification (CTC) is a sequence-level loss that
has been successfully applied to train recurrent neural network (RNN) models for
automatic speech recognition. However, one major weakness of CTC is the conditional
independence assumption that makes it difficult for the model to learn label dependencies.
In this paper, we propose stimulated CTC, which uses stimulated learning to help
CTC models learn label dependencies implicitly by using an auxiliary RNN to generate
the appropriate stimuli. This stimuli comes in the form of an additional stimulation
loss term which encourages the model to learn said label dependencies. The auxiliary
network is only used during training and the inference model has the same structure
as a standard CTC model. The proposed stimulated CTC model achieves about 35%
relative character error rate improvements on a synthetic gesture keyboard recognition
task and over 30% relative word error rate improvements on the Librispeech automatic
speech recognition tasks over a baseline model trained with CTC only.
author:
- first_name: Jahn
full_name: Heymann, Jahn
last_name: Heymann
- first_name: Bo Li
full_name: Khe Chai Sim, Bo Li
last_name: Khe Chai Sim
citation:
ama: 'Heymann J, Khe Chai Sim BL. Improving CTC Using Stimulated Learning for Sequence
Modeling. In: ICASSP 2019, Brighton, UK. ; 2019.'
apa: Heymann, J., & Khe Chai Sim, B. L. (2019). Improving CTC Using Stimulated
Learning for Sequence Modeling. In ICASSP 2019, Brighton, UK.
bibtex: '@inproceedings{Heymann_Khe Chai Sim_2019, title={Improving CTC Using Stimulated
Learning for Sequence Modeling}, booktitle={ICASSP 2019, Brighton, UK}, author={Heymann,
Jahn and Khe Chai Sim, Bo Li}, year={2019} }'
chicago: Heymann, Jahn, and Bo Li Khe Chai Sim. “Improving CTC Using Stimulated
Learning for Sequence Modeling.” In ICASSP 2019, Brighton, UK, 2019.
ieee: J. Heymann and B. L. Khe Chai Sim, “Improving CTC Using Stimulated Learning
for Sequence Modeling,” in ICASSP 2019, Brighton, UK, 2019.
mla: Heymann, Jahn, and Bo Li Khe Chai Sim. “Improving CTC Using Stimulated Learning
for Sequence Modeling.” ICASSP 2019, Brighton, UK, 2019.
short: 'J. Heymann, B.L. Khe Chai Sim, in: ICASSP 2019, Brighton, UK, 2019.'
date_created: 2020-02-06T07:22:47Z
date_updated: 2022-01-06T06:52:35Z
ddc:
- '000'
department:
- _id: '54'
file:
- access_level: open_access
content_type: application/pdf
creator: huesera
date_created: 2020-02-06T07:24:26Z
date_updated: 2020-02-06T07:24:26Z
file_id: '15813'
file_name: ICASSP_2019_Heymann_1_Paper.pdf
file_size: 239665
relation: main_file
file_date_updated: 2020-02-06T07:24:26Z
has_accepted_license: '1'
language:
- iso: eng
oa: '1'
project:
- _id: '52'
name: Computing Resources Provided by the Paderborn Center for Parallel Computing
publication: ICASSP 2019, Brighton, UK
status: public
title: Improving CTC Using Stimulated Learning for Sequence Modeling
type: conference
user_id: '59789'
year: '2019'
...
---
_id: '15816'
abstract:
- lang: eng
text: 'Despite the strong modeling power of neural network acoustic models, speech
enhancement has been shown to deliver additional word error rate improvements
if multi-channel data is available. However, there has been a longstanding debate
whether enhancement should also be carried out on the ASR training data. In an
extensive experimental evaluation on the acoustically very challenging CHiME-5
dinner party data we show that: (i) cleaning up the training data can lead to
substantial error rate reductions, and (ii) enhancement in training is advisable
as long as enhancement in test is at least as strong as in training. This approach
stands in contrast and delivers larger gains than the common strategy reported
in the literature to augment the training database with additional artificially
degraded speech. Together with an acoustic model topology consisting of initial
CNN layers followed by factorized TDNN layers we achieve with 41.6% and 43.2%
WER on the DEV and EVAL test sets, respectively, a new single-system state-of-the-art
result on the CHiME-5 data. This is a 8% relative improvement compared to the
best word error rate published so far for a speech recognizer without system combination.'
author:
- first_name: Catalin
full_name: Zorila, Catalin
last_name: Zorila
- first_name: Christoph
full_name: Boeddeker, Christoph
id: '40767'
last_name: Boeddeker
- first_name: Rama
full_name: Doddipatla, Rama
last_name: Doddipatla
- first_name: Reinhold
full_name: Haeb-Umbach, Reinhold
id: '242'
last_name: Haeb-Umbach
citation:
ama: 'Zorila C, Boeddeker C, Doddipatla R, Haeb-Umbach R. An Investigation Into
the Effectiveness of Enhancement in ASR Training and Test for Chime-5 Dinner Party
Transcription. In: ASRU 2019, Sentosa, Singapore. ; 2019.'
apa: Zorila, C., Boeddeker, C., Doddipatla, R., & Haeb-Umbach, R. (2019). An
Investigation Into the Effectiveness of Enhancement in ASR Training and Test for
Chime-5 Dinner Party Transcription. In ASRU 2019, Sentosa, Singapore.
bibtex: '@inproceedings{Zorila_Boeddeker_Doddipatla_Haeb-Umbach_2019, title={An
Investigation Into the Effectiveness of Enhancement in ASR Training and Test for
Chime-5 Dinner Party Transcription}, booktitle={ASRU 2019, Sentosa, Singapore},
author={Zorila, Catalin and Boeddeker, Christoph and Doddipatla, Rama and Haeb-Umbach,
Reinhold}, year={2019} }'
chicago: Zorila, Catalin, Christoph Boeddeker, Rama Doddipatla, and Reinhold Haeb-Umbach.
“An Investigation Into the Effectiveness of Enhancement in ASR Training and Test
for Chime-5 Dinner Party Transcription.” In ASRU 2019, Sentosa, Singapore,
2019.
ieee: C. Zorila, C. Boeddeker, R. Doddipatla, and R. Haeb-Umbach, “An Investigation
Into the Effectiveness of Enhancement in ASR Training and Test for Chime-5 Dinner
Party Transcription,” in ASRU 2019, Sentosa, Singapore, 2019.
mla: Zorila, Catalin, et al. “An Investigation Into the Effectiveness of Enhancement
in ASR Training and Test for Chime-5 Dinner Party Transcription.” ASRU 2019,
Sentosa, Singapore, 2019.
short: 'C. Zorila, C. Boeddeker, R. Doddipatla, R. Haeb-Umbach, in: ASRU 2019, Sentosa,
Singapore, 2019.'
date_created: 2020-02-06T07:35:08Z
date_updated: 2022-01-06T06:52:37Z
ddc:
- '000'
department:
- _id: '54'
file:
- access_level: open_access
content_type: application/pdf
creator: huesera
date_created: 2020-02-06T07:42:42Z
date_updated: 2020-02-06T07:42:42Z
file_id: '15817'
file_name: ASRU_2019_Boeddeker_Paper.pdf
file_size: 200256
relation: main_file
- access_level: open_access
content_type: application/pdf
creator: huesera
date_created: 2020-02-06T07:42:55Z
date_updated: 2020-02-06T07:42:55Z
file_id: '15818'
file_name: ASRU_2019_Boeddeker_Poster.pdf
file_size: 123963
relation: main_file
file_date_updated: 2020-02-06T07:42:55Z
has_accepted_license: '1'
language:
- iso: eng
oa: '1'
project:
- _id: '52'
name: Computing Resources Provided by the Paderborn Center for Parallel Computing
publication: ASRU 2019, Sentosa, Singapore
status: public
title: An Investigation Into the Effectiveness of Enhancement in ASR Training and
Test for Chime-5 Dinner Party Transcription
type: conference
user_id: '59789'
year: '2019'
...
---
_id: '14822'
abstract:
- lang: eng
text: Multi-talker speech and moving speakers still pose a significant challenge
to automatic speech recognition systems. Assuming an enrollment utterance of the
target speakeris available, the so-called SpeakerBeam concept has been recently
proposed to extract the target speaker from a speech mixture. If multi-channel
input is available, spatial properties of the speaker can be exploited to support
the source extraction. In this contribution we investigate different approaches
to exploit such spatial information. In particular, we are interested in the question,
how useful this information is if the target speaker changes his/her position.
To this end, we present a SpeakerBeam-based source extraction network that is
adapted to work on moving speakers by recursively updating the beamformer coefficients.
Experimental results are presented on two data sets, one with articially created
room impulse responses, and one with real room impulse responses and noise recorded
in a conference room. Interestingly, spatial features turn out to be advantageous
even if the speaker position changes.
author:
- first_name: Jens
full_name: Heitkaemper, Jens
id: '27643'
last_name: Heitkaemper
- first_name: Thomas
full_name: Feher, Thomas
last_name: Feher
- first_name: Michael
full_name: Freitag, Michael
last_name: Freitag
- first_name: Reinhold
full_name: Haeb-Umbach, Reinhold
id: '242'
last_name: Haeb-Umbach
citation:
ama: 'Heitkaemper J, Feher T, Freitag M, Haeb-Umbach R. A Study on Online Source
Extraction in the Presence of Changing Speaker Positions. In: International
Conference on Statistical Language and Speech Processing 2019, Ljubljana, Slovenia.
; 2019.'
apa: Heitkaemper, J., Feher, T., Freitag, M., & Haeb-Umbach, R. (2019). A Study
on Online Source Extraction in the Presence of Changing Speaker Positions. In
International Conference on Statistical Language and Speech Processing 2019,
Ljubljana, Slovenia.
bibtex: '@inproceedings{Heitkaemper_Feher_Freitag_Haeb-Umbach_2019, title={A Study
on Online Source Extraction in the Presence of Changing Speaker Positions}, booktitle={International
Conference on Statistical Language and Speech Processing 2019, Ljubljana, Slovenia},
author={Heitkaemper, Jens and Feher, Thomas and Freitag, Michael and Haeb-Umbach,
Reinhold}, year={2019} }'
chicago: Heitkaemper, Jens, Thomas Feher, Michael Freitag, and Reinhold Haeb-Umbach.
“A Study on Online Source Extraction in the Presence of Changing Speaker Positions.”
In International Conference on Statistical Language and Speech Processing 2019,
Ljubljana, Slovenia, 2019.
ieee: J. Heitkaemper, T. Feher, M. Freitag, and R. Haeb-Umbach, “A Study on Online
Source Extraction in the Presence of Changing Speaker Positions,” in International
Conference on Statistical Language and Speech Processing 2019, Ljubljana, Slovenia,
2019.
mla: Heitkaemper, Jens, et al. “A Study on Online Source Extraction in the Presence
of Changing Speaker Positions.” International Conference on Statistical Language
and Speech Processing 2019, Ljubljana, Slovenia, 2019.
short: 'J. Heitkaemper, T. Feher, M. Freitag, R. Haeb-Umbach, in: International
Conference on Statistical Language and Speech Processing 2019, Ljubljana, Slovenia,
2019.'
date_created: 2019-11-06T09:43:03Z
date_updated: 2022-01-06T06:52:06Z
ddc:
- '006'
department:
- _id: '54'
file:
- access_level: open_access
content_type: application/pdf
creator: huesera
date_created: 2019-11-06T10:02:26Z
date_updated: 2019-11-08T07:47:12Z
file_id: '14823'
file_name: SLSP_2019_Heitkaemper_Paper.pdf
file_size: 578595
relation: main_file
file_date_updated: 2019-11-08T07:47:12Z
has_accepted_license: '1'
language:
- iso: eng
oa: '1'
project:
- _id: '52'
name: Computing Resources Provided by the Paderborn Center for Parallel Computing
publication: International Conference on Statistical Language and Speech Processing
2019, Ljubljana, Slovenia
status: public
title: A Study on Online Source Extraction in the Presence of Changing Speaker Positions
type: conference
user_id: '59789'
year: '2019'
...
---
_id: '14824'
abstract:
- lang: eng
text: This paper deals with multi-channel speech recognition in scenarios with multiple
speakers. Recently, the spectral characteristics of a target speaker, extracted
from an adaptation utterance, have been used to guide a neural network mask estimator
to focus on that speaker. In this work we present two variants of speakeraware
neural networks, which exploit both spectral and spatial information to allow
better discrimination between target and interfering speakers. Thus, we introduce
either a spatial preprocessing prior to the mask estimation or a spatial plus
spectral speaker characterization block whose output is directly fed into the
neural mask estimator. The target speaker’s spectral and spatial signature is
extracted from an adaptation utterance recorded at the beginning of a session.
We further adapt the architecture for low-latency processing by means of block-online
beamforming that recursively updates the signal statistics. Experimental results
show that the additional spatial information clearly improves source extraction,
in particular in the same-gender case, and that our proposal achieves state-of-the-art
performance in terms of distortion reduction and recognition accuracy.
author:
- first_name: Juan M.
full_name: Martin-Donas, Juan M.
last_name: Martin-Donas
- first_name: Jens
full_name: Heitkaemper, Jens
id: '27643'
last_name: Heitkaemper
- first_name: Reinhold
full_name: Haeb-Umbach, Reinhold
id: '242'
last_name: Haeb-Umbach
- first_name: Angel M.
full_name: Gomez, Angel M.
last_name: Gomez
- first_name: Antonio M.
full_name: Peinado, Antonio M.
last_name: Peinado
citation:
ama: 'Martin-Donas JM, Heitkaemper J, Haeb-Umbach R, Gomez AM, Peinado AM. Multi-Channel
Block-Online Source Extraction based on Utterance Adaptation. In: INTERSPEECH
2019, Graz, Austria. ; 2019.'
apa: Martin-Donas, J. M., Heitkaemper, J., Haeb-Umbach, R., Gomez, A. M., &
Peinado, A. M. (2019). Multi-Channel Block-Online Source Extraction based on Utterance
Adaptation. In INTERSPEECH 2019, Graz, Austria.
bibtex: '@inproceedings{Martin-Donas_Heitkaemper_Haeb-Umbach_Gomez_Peinado_2019,
title={Multi-Channel Block-Online Source Extraction based on Utterance Adaptation},
booktitle={INTERSPEECH 2019, Graz, Austria}, author={Martin-Donas, Juan M. and
Heitkaemper, Jens and Haeb-Umbach, Reinhold and Gomez, Angel M. and Peinado, Antonio
M.}, year={2019} }'
chicago: Martin-Donas, Juan M., Jens Heitkaemper, Reinhold Haeb-Umbach, Angel M.
Gomez, and Antonio M. Peinado. “Multi-Channel Block-Online Source Extraction Based
on Utterance Adaptation.” In INTERSPEECH 2019, Graz, Austria, 2019.
ieee: J. M. Martin-Donas, J. Heitkaemper, R. Haeb-Umbach, A. M. Gomez, and A. M.
Peinado, “Multi-Channel Block-Online Source Extraction based on Utterance Adaptation,”
in INTERSPEECH 2019, Graz, Austria, 2019.
mla: Martin-Donas, Juan M., et al. “Multi-Channel Block-Online Source Extraction
Based on Utterance Adaptation.” INTERSPEECH 2019, Graz, Austria, 2019.
short: 'J.M. Martin-Donas, J. Heitkaemper, R. Haeb-Umbach, A.M. Gomez, A.M. Peinado,
in: INTERSPEECH 2019, Graz, Austria, 2019.'
date_created: 2019-11-06T10:04:49Z
date_updated: 2022-01-06T06:52:07Z
ddc:
- '000'
department:
- _id: '54'
file:
- access_level: open_access
content_type: application/pdf
creator: huesera
date_created: 2019-11-06T10:07:15Z
date_updated: 2019-11-08T07:46:37Z
file_id: '14825'
file_name: INTERSPEECH_2019_Heitkaemper_Paper.pdf
file_size: 225689
relation: main_file
file_date_updated: 2019-11-08T07:46:37Z
has_accepted_license: '1'
language:
- iso: eng
oa: '1'
project:
- _id: '52'
name: Computing Resources Provided by the Paderborn Center for Parallel Computing
publication: INTERSPEECH 2019, Graz, Austria
status: public
title: Multi-Channel Block-Online Source Extraction based on Utterance Adaptation
type: conference
user_id: '59789'
year: '2019'
...
---
_id: '14826'
abstract:
- lang: eng
text: In this paper, we present Hitachi and Paderborn University’s joint effort
for automatic speech recognition (ASR) in a dinner party scenario. The main challenges
of ASR systems for dinner party recordings obtained by multiple microphone arrays
are (1) heavy speech overlaps, (2) severe noise and reverberation, (3) very natural
onversational content, and possibly (4) insufficient training data. As an example
of a dinner party scenario, we have chosen the data presented during the CHiME-5
speech recognition challenge, where the baseline ASR had a 73.3% word error rate
(WER), and even the best performing system at the CHiME-5 challenge had a 46.1%
WER. We extensively investigated a combination of the guided source separation-based
speech enhancement technique and an already proposed strong ASR backend and found
that a tight combination of these techniques provided substantial accuracy improvements.
Our final system achieved WERs of 39.94% and 41.64% for the development and evaluation
data, respectively, both of which are the best published results for the dataset.
We also investigated with additional training data on the official small data
in the CHiME-5 corpus to assess the intrinsic difficulty of this ASR task.
author:
- first_name: Naoyuki
full_name: Kanda, Naoyuki
last_name: Kanda
- 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: Yusuke
full_name: Fujita, Yusuke
last_name: Fujita
- first_name: Shota
full_name: Horiguchi, Shota
last_name: Horiguchi
- first_name: Reinhold
full_name: Haeb-Umbach, Reinhold
id: '242'
last_name: Haeb-Umbach
citation:
ama: 'Kanda N, Boeddeker C, Heitkaemper J, Fujita Y, Horiguchi S, Haeb-Umbach R.
Guided Source Separation Meets a Strong ASR Backend: Hitachi/Paderborn University
Joint Investigation for Dinner Party ASR. In: INTERSPEECH 2019, Graz, Austria.
; 2019.'
apa: 'Kanda, N., Boeddeker, C., Heitkaemper, J., Fujita, Y., Horiguchi, S., &
Haeb-Umbach, R. (2019). Guided Source Separation Meets a Strong ASR Backend: Hitachi/Paderborn
University Joint Investigation for Dinner Party ASR. In INTERSPEECH 2019, Graz,
Austria.'
bibtex: '@inproceedings{Kanda_Boeddeker_Heitkaemper_Fujita_Horiguchi_Haeb-Umbach_2019,
title={Guided Source Separation Meets a Strong ASR Backend: Hitachi/Paderborn
University Joint Investigation for Dinner Party ASR}, booktitle={INTERSPEECH 2019,
Graz, Austria}, author={Kanda, Naoyuki and Boeddeker, Christoph and Heitkaemper,
Jens and Fujita, Yusuke and Horiguchi, Shota and Haeb-Umbach, Reinhold}, year={2019}
}'
chicago: 'Kanda, Naoyuki, Christoph Boeddeker, Jens Heitkaemper, Yusuke Fujita,
Shota Horiguchi, and Reinhold Haeb-Umbach. “Guided Source Separation Meets a Strong
ASR Backend: Hitachi/Paderborn University Joint Investigation for Dinner Party
ASR.” In INTERSPEECH 2019, Graz, Austria, 2019.'
ieee: 'N. Kanda, C. Boeddeker, J. Heitkaemper, Y. Fujita, S. Horiguchi, and R. Haeb-Umbach,
“Guided Source Separation Meets a Strong ASR Backend: Hitachi/Paderborn University
Joint Investigation for Dinner Party ASR,” in INTERSPEECH 2019, Graz, Austria,
2019.'
mla: 'Kanda, Naoyuki, et al. “Guided Source Separation Meets a Strong ASR Backend:
Hitachi/Paderborn University Joint Investigation for Dinner Party ASR.” INTERSPEECH
2019, Graz, Austria, 2019.'
short: 'N. Kanda, C. Boeddeker, J. Heitkaemper, Y. Fujita, S. Horiguchi, R. Haeb-Umbach,
in: INTERSPEECH 2019, Graz, Austria, 2019.'
date_created: 2019-11-06T10:08:49Z
date_updated: 2022-01-06T06:52:07Z
ddc:
- '000'
department:
- _id: '54'
file:
- access_level: open_access
content_type: application/pdf
creator: huesera
date_created: 2019-11-06T10:10:23Z
date_updated: 2019-11-08T07:45:15Z
file_id: '14827'
file_name: INTERSPEECH_2019_Boeddeker_Paper.pdf
file_size: 216202
relation: main_file
file_date_updated: 2019-11-08T07:45:15Z
has_accepted_license: '1'
language:
- iso: eng
oa: '1'
project:
- _id: '52'
name: Computing Resources Provided by the Paderborn Center for Parallel Computing
publication: INTERSPEECH 2019, Graz, Austria
status: public
title: 'Guided Source Separation Meets a Strong ASR Backend: Hitachi/Paderborn University
Joint Investigation for Dinner Party ASR'
type: conference
user_id: '59789'
year: '2019'
...
---
_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'
...