---
_id: '11759'
abstract:
- lang: eng
  text: 'Variational Autoencoders (VAEs) have been shown to provide efficient neural-network-based
    approximate Bayesian inference for observation models for which exact inference
    is intractable. Its extension, the so-called Structured VAE (SVAE) allows inference
    in the presence of both discrete and continuous latent variables. Inspired by
    this extension, we developed a VAE with Hidden Markov Models (HMMs) as latent
    models. We applied the resulting HMM-VAE to the task of acoustic unit discovery
    in a zero resource scenario. Starting from an initial model based on variational
    inference in an HMM with Gaussian Mixture Model (GMM) emission probabilities,
    the accuracy of the acoustic unit discovery could be significantly improved by
    the HMM-VAE. In doing so we were able to demonstrate for an unsupervised learning
    task what is well-known in the supervised learning case: Neural networks provide
    superior modeling power compared to GMMs.'
author:
- first_name: Janek
  full_name: Ebbers, Janek
  id: '34851'
  last_name: Ebbers
- 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: Thomas
  full_name: Glarner, Thomas
  id: '14169'
  last_name: Glarner
- first_name: Reinhold
  full_name: Haeb-Umbach, Reinhold
  id: '242'
  last_name: Haeb-Umbach
- first_name: Bhiksha
  full_name: Raj, Bhiksha
  last_name: Raj
citation:
  ama: 'Ebbers J, Heymann J, Drude L, Glarner T, Haeb-Umbach R, Raj B. Hidden Markov
    Model Variational Autoencoder for Acoustic Unit Discovery. In: <i>INTERSPEECH
    2017, Stockholm, Schweden</i>. ; 2017.'
  apa: Ebbers, J., Heymann, J., Drude, L., Glarner, T., Haeb-Umbach, R., &#38; Raj,
    B. (2017). Hidden Markov Model Variational Autoencoder for Acoustic Unit Discovery.
    <i>INTERSPEECH 2017, Stockholm, Schweden</i>.
  bibtex: '@inproceedings{Ebbers_Heymann_Drude_Glarner_Haeb-Umbach_Raj_2017, title={Hidden
    Markov Model Variational Autoencoder for Acoustic Unit Discovery}, booktitle={INTERSPEECH
    2017, Stockholm, Schweden}, author={Ebbers, Janek and Heymann, Jahn and Drude,
    Lukas and Glarner, Thomas and Haeb-Umbach, Reinhold and Raj, Bhiksha}, year={2017}
    }'
  chicago: Ebbers, Janek, Jahn Heymann, Lukas Drude, Thomas Glarner, Reinhold Haeb-Umbach,
    and Bhiksha Raj. “Hidden Markov Model Variational Autoencoder for Acoustic Unit
    Discovery.” In <i>INTERSPEECH 2017, Stockholm, Schweden</i>, 2017.
  ieee: J. Ebbers, J. Heymann, L. Drude, T. Glarner, R. Haeb-Umbach, and B. Raj, “Hidden
    Markov Model Variational Autoencoder for Acoustic Unit Discovery,” 2017.
  mla: Ebbers, Janek, et al. “Hidden Markov Model Variational Autoencoder for Acoustic
    Unit Discovery.” <i>INTERSPEECH 2017, Stockholm, Schweden</i>, 2017.
  short: 'J. Ebbers, J. Heymann, L. Drude, T. Glarner, R. Haeb-Umbach, B. Raj, in:
    INTERSPEECH 2017, Stockholm, Schweden, 2017.'
date_created: 2019-07-12T05:27:42Z
date_updated: 2023-11-22T08:29:06Z
department:
- _id: '54'
language:
- iso: eng
main_file_link:
- open_access: '1'
  url: https://groups.uni-paderborn.de/nt/pubs/2017/INTERSPEECH_2017_Ebbers_paper.pdf
oa: '1'
publication: INTERSPEECH 2017, Stockholm, Schweden
quality_controlled: '1'
related_material:
  link:
  - description: Poster
    relation: supplementary_material
    url: https://groups.uni-paderborn.de/nt/pubs/2017/INTERSPEECH_2017_Ebbers_poster.pdf
  - description: Slides
    relation: supplementary_material
    url: https://groups.uni-paderborn.de/nt/pubs/2017/INTERSPEECH_2017_Ebbers_slides.pdf
status: public
title: Hidden Markov Model Variational Autoencoder for Acoustic Unit Discovery
type: conference
user_id: '34851'
year: '2017'
...
---
_id: '11895'
abstract:
- lang: eng
  text: Multi-channel speech enhancement algorithms rely on a synchronous sampling
    of the microphone signals. This, however, cannot always be guaranteed, especially
    if the sensors are distributed in an environment. To avoid performance degradation
    the sampling rate offset needs to be estimated and compensated for. In this contribution
    we extend the recently proposed coherence drift based method in two important
    directions. First, the increasing phase shift in the short-time Fourier transform
    domain is estimated from the coherence drift in a Matched Filterlike fashion,
    where intermediate estimates are weighted by their instantaneous SNR. Second,
    an observed bias is removed by iterating between offset estimation and compensation
    by resampling a couple of times. The effectiveness of the proposed method is demonstrated
    by speech recognition results on the output of a beamformer with and without sampling
    rate offset compensation between the input channels. We compare MVDR and maximum-SNR
    beamformers in reverberant environments and further show that both benefit from
    a novel phase normalization, which we also propose in this contribution.
author:
- first_name: Joerg
  full_name: Schmalenstroeer, Joerg
  id: '460'
  last_name: Schmalenstroeer
- 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: 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: 'Schmalenstroeer J, Heymann J, Drude L, Boeddeker C, Haeb-Umbach R. Multi-Stage
    Coherence Drift Based Sampling Rate Synchronization for Acoustic Beamforming.
    In: <i>IEEE 19th International Workshop on Multimedia Signal Processing (MMSP)</i>.
    ; 2017.'
  apa: Schmalenstroeer, J., Heymann, J., Drude, L., Boeddeker, C., &#38; Haeb-Umbach,
    R. (2017). Multi-Stage Coherence Drift Based Sampling Rate Synchronization for
    Acoustic Beamforming. <i>IEEE 19th International Workshop on Multimedia Signal
    Processing (MMSP)</i>.
  bibtex: '@inproceedings{Schmalenstroeer_Heymann_Drude_Boeddeker_Haeb-Umbach_2017,
    title={Multi-Stage Coherence Drift Based Sampling Rate Synchronization for Acoustic
    Beamforming}, booktitle={IEEE 19th International Workshop on Multimedia Signal
    Processing (MMSP)}, author={Schmalenstroeer, Joerg and Heymann, Jahn and Drude,
    Lukas and Boeddeker, Christoph and Haeb-Umbach, Reinhold}, year={2017} }'
  chicago: Schmalenstroeer, Joerg, Jahn Heymann, Lukas Drude, Christoph Boeddeker,
    and Reinhold Haeb-Umbach. “Multi-Stage Coherence Drift Based Sampling Rate Synchronization
    for Acoustic Beamforming.” In <i>IEEE 19th International Workshop on Multimedia
    Signal Processing (MMSP)</i>, 2017.
  ieee: J. Schmalenstroeer, J. Heymann, L. Drude, C. Boeddeker, and R. Haeb-Umbach,
    “Multi-Stage Coherence Drift Based Sampling Rate Synchronization for Acoustic
    Beamforming,” 2017.
  mla: Schmalenstroeer, Joerg, et al. “Multi-Stage Coherence Drift Based Sampling
    Rate Synchronization for Acoustic Beamforming.” <i>IEEE 19th International Workshop
    on Multimedia Signal Processing (MMSP)</i>, 2017.
  short: 'J. Schmalenstroeer, J. Heymann, L. Drude, C. Boeddeker, R. Haeb-Umbach,
    in: IEEE 19th International Workshop on Multimedia Signal Processing (MMSP), 2017.'
date_created: 2019-07-12T05:30:20Z
date_updated: 2023-10-26T08:12:05Z
department:
- _id: '54'
language:
- iso: eng
main_file_link:
- open_access: '1'
  url: https://groups.uni-paderborn.de/nt/pubs/2017/MMSP_2017_SchHaeb.pdf
oa: '1'
publication: IEEE 19th International Workshop on Multimedia Signal Processing (MMSP)
quality_controlled: '1'
related_material:
  link:
  - description: Poster
    relation: supplementary_material
    url: https://groups.uni-paderborn.de/nt/pubs/2017/MMSP_2017_SchHaeb_poster.pdf
status: public
title: Multi-Stage Coherence Drift Based Sampling Rate Synchronization for Acoustic
  Beamforming
type: conference
user_id: '460'
year: '2017'
...
---
_id: '11773'
abstract:
- lang: eng
  text: In this paper we present an algorithm for the detection of moving targets
    in sight of an automotive radar sensor which can handle distorted ego-velocity
    information. In situations where biased or none velocity information is provided
    from the ego-vehicle, the algorithm is able to estimate the ego-velocity based
    on previously detected stationary targets with high accuracy, subsequently used
    for the target classification. Compared to existing ego-velocity algorithms our
    approach provides fast and efficient inference without sacrificing the practical
    classification accuracy. Other than that the algorithm is characterized by simple
    parameterization and little but appropriate model assumptions for high accurate
    production automotive radar sensors.
author:
- first_name: Christopher
  full_name: Grimm, Christopher
  last_name: Grimm
- 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, Farhoud R, Fei T, Warsitz E, Haeb-Umbach R. Detection of Moving Targets
    in Automotive Radar with Distorted Ego-Velocity Information. In: <i>IEEE Microwaves,
    Radar and Remote Sensing Symposium (MRRS)</i>. ; 2017.'
  apa: Grimm, C., Farhoud, R., Fei, T., Warsitz, E., &#38; Haeb-Umbach, R. (2017).
    Detection of Moving Targets in Automotive Radar with Distorted Ego-Velocity Information.
    <i>IEEE Microwaves, Radar and Remote Sensing Symposium (MRRS)</i>.
  bibtex: '@inproceedings{Grimm_Farhoud_Fei_Warsitz_Haeb-Umbach_2017, title={Detection
    of Moving Targets in Automotive Radar with Distorted Ego-Velocity Information},
    booktitle={IEEE Microwaves, Radar and Remote Sensing Symposium (MRRS)}, author={Grimm,
    Christopher and Farhoud, Ridha and Fei, Tai and Warsitz, Ernst and Haeb-Umbach,
    Reinhold}, year={2017} }'
  chicago: Grimm, Christopher, Ridha Farhoud, Tai Fei, Ernst Warsitz, and Reinhold
    Haeb-Umbach. “Detection of Moving Targets in Automotive Radar with Distorted Ego-Velocity
    Information.” In <i>IEEE Microwaves, Radar and Remote Sensing Symposium (MRRS)</i>,
    2017.
  ieee: C. Grimm, R. Farhoud, T. Fei, E. Warsitz, and R. Haeb-Umbach, “Detection of
    Moving Targets in Automotive Radar with Distorted Ego-Velocity Information,” 2017.
  mla: Grimm, Christopher, et al. “Detection of Moving Targets in Automotive Radar
    with Distorted Ego-Velocity Information.” <i>IEEE Microwaves, Radar and Remote
    Sensing Symposium (MRRS)</i>, 2017.
  short: 'C. Grimm, R. Farhoud, T. Fei, E. Warsitz, R. Haeb-Umbach, in: IEEE Microwaves,
    Radar and Remote Sensing Symposium (MRRS), 2017.'
date_created: 2019-07-12T05:27:59Z
date_updated: 2023-11-20T16:38:11Z
department:
- _id: '54'
language:
- iso: eng
main_file_link:
- open_access: '1'
  url: https://groups.uni-paderborn.de/nt/pubs/2017/MRRS_2017_GrimmHaeb_paper.pdf
oa: '1'
publication: IEEE Microwaves, Radar and Remote Sensing Symposium (MRRS)
quality_controlled: '1'
status: public
title: Detection of Moving Targets in Automotive Radar with Distorted Ego-Velocity
  Information
type: conference
user_id: '242'
year: '2017'
...
---
_id: '11738'
abstract:
- lang: eng
  text: 'In this contribution we investigate a priori signal-to-noise ratio (SNR)
    estimation, a crucial component of a single-channel speech enhancement system
    based on spectral subtraction. The majority of the state-of-the art a priori SNR
    estimators work in the power spectral domain, which is, however, not confirmed
    to be the optimal domain for the estimation. Motivated by the generalized spectral
    subtraction rule, we show how the estimation of the a priori SNR can be formulated
    in the so called generalized SNR domain. This formulation allows to generalize
    the widely used decision directed (DD) approach. An experimental investigation
    with different noise types reveals the superiority of the generalized DD approach
    over the conventional DD approach in terms of both the mean opinion score - listening
    quality objective measure and the output global SNR in the medium to high input
    SNR regime, while we show that the power spectrum is the optimal domain for low
    SNR. We further develop a parameterization which adjusts the domain of estimation
    automatically according to the estimated input global SNR. Index Terms: single-channel
    speech enhancement, a priori SNR estimation, generalized spectral subtraction'
author:
- first_name: Aleksej
  full_name: Chinaev, Aleksej
  last_name: Chinaev
- first_name: Reinhold
  full_name: Haeb-Umbach, Reinhold
  id: '242'
  last_name: Haeb-Umbach
citation:
  ama: 'Chinaev A, Haeb-Umbach R. A Priori SNR Estimation Using a Generalized Decision
    Directed Approach. In: <i>INTERSPEECH 2016, San Francisco, USA</i>. ; 2016.'
  apa: Chinaev, A., &#38; Haeb-Umbach, R. (2016). A Priori SNR Estimation Using a
    Generalized Decision Directed Approach. In <i>INTERSPEECH 2016, San Francisco,
    USA</i>.
  bibtex: '@inproceedings{Chinaev_Haeb-Umbach_2016, title={A Priori SNR Estimation
    Using a Generalized Decision Directed Approach}, booktitle={INTERSPEECH 2016,
    San Francisco, USA}, author={Chinaev, Aleksej and Haeb-Umbach, Reinhold}, year={2016}
    }'
  chicago: Chinaev, Aleksej, and Reinhold Haeb-Umbach. “A Priori SNR Estimation Using
    a Generalized Decision Directed Approach.” In <i>INTERSPEECH 2016, San Francisco,
    USA</i>, 2016.
  ieee: A. Chinaev and R. Haeb-Umbach, “A Priori SNR Estimation Using a Generalized
    Decision Directed Approach,” in <i>INTERSPEECH 2016, San Francisco, USA</i>, 2016.
  mla: Chinaev, Aleksej, and Reinhold Haeb-Umbach. “A Priori SNR Estimation Using
    a Generalized Decision Directed Approach.” <i>INTERSPEECH 2016, San Francisco,
    USA</i>, 2016.
  short: 'A. Chinaev, R. Haeb-Umbach, in: INTERSPEECH 2016, San Francisco, USA, 2016.'
date_created: 2019-07-12T05:27:18Z
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/2016/ChHa16.pdf
oa: '1'
publication: INTERSPEECH 2016, San Francisco, USA
related_material:
  link:
  - description: Poster
    relation: supplementary_material
    url: https://groups.uni-paderborn.de/nt/pubs/2016/ChHa16_Poster.pdf
status: public
title: A Priori SNR Estimation Using a Generalized Decision Directed Approach
type: conference
user_id: '44006'
year: '2016'
...
---
_id: '11743'
abstract:
- lang: eng
  text: This contribution introduces a novel causal a priori signal-to-noise ratio
    (SNR) estimator for single-channel speech enhancement. To exploit the advantages
    of the generalized spectral subtraction, a normalized ?-order magnitude (NAOM)
    domain is introduced where an a priori SNR estimation is carried out. In this
    domain, the NAOM coefficients of noise and clean speech signals are modeled by
    a Weibull distribution and aWeibullmixturemodel (WMM), respectively. While the
    parameters of the noise model are calculated from the noise power spectral density
    estimates, the speechWMM parameters are estimated from the noisy signal by applying
    a causal Expectation-Maximization algorithm. Further a maximum a posteriori estimate
    of the a priori SNR is developed. The experiments in different noisy environments
    show the superiority of the proposed estimator compared to the well-known decision-directed
    approach in terms of estimation error, estimator variance and speech quality of
    the enhanced signals when used for speech enhancement.
author:
- first_name: Aleksej
  full_name: Chinaev, Aleksej
  last_name: Chinaev
- 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
citation:
  ama: 'Chinaev A, Heitkaemper J, Haeb-Umbach R. A Priori SNR Estimation Using Weibull
    Mixture Model. In: <i>12. ITG Fachtagung Sprachkommunikation (ITG 2016)</i>. ;
    2016.'
  apa: Chinaev, A., Heitkaemper, J., &#38; Haeb-Umbach, R. (2016). A Priori SNR Estimation
    Using Weibull Mixture Model. In <i>12. ITG Fachtagung Sprachkommunikation (ITG
    2016)</i>.
  bibtex: '@inproceedings{Chinaev_Heitkaemper_Haeb-Umbach_2016, title={A Priori SNR
    Estimation Using Weibull Mixture Model}, booktitle={12. ITG Fachtagung Sprachkommunikation
    (ITG 2016)}, author={Chinaev, Aleksej and Heitkaemper, Jens and Haeb-Umbach, Reinhold},
    year={2016} }'
  chicago: Chinaev, Aleksej, Jens Heitkaemper, and Reinhold Haeb-Umbach. “A Priori
    SNR Estimation Using Weibull Mixture Model.” In <i>12. ITG Fachtagung Sprachkommunikation
    (ITG 2016)</i>, 2016.
  ieee: A. Chinaev, J. Heitkaemper, and R. Haeb-Umbach, “A Priori SNR Estimation Using
    Weibull Mixture Model,” in <i>12. ITG Fachtagung Sprachkommunikation (ITG 2016)</i>,
    2016.
  mla: Chinaev, Aleksej, et al. “A Priori SNR Estimation Using Weibull Mixture Model.”
    <i>12. ITG Fachtagung Sprachkommunikation (ITG 2016)</i>, 2016.
  short: 'A. Chinaev, J. Heitkaemper, R. Haeb-Umbach, in: 12. ITG Fachtagung Sprachkommunikation
    (ITG 2016), 2016.'
date_created: 2019-07-12T05:27:24Z
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/2016/ChHeiHa16.pdf
oa: '1'
publication: 12. ITG Fachtagung Sprachkommunikation (ITG 2016)
related_material:
  link:
  - description: Presentation
    relation: supplementary_material
    url: https://groups.uni-paderborn.de/nt/pubs/2016/ChHeiHa16_Presentation.pdf
status: public
title: A Priori SNR Estimation Using Weibull Mixture Model
type: conference
user_id: '44006'
year: '2016'
...
---
_id: '11744'
abstract:
- lang: eng
  text: A noise power spectral density (PSD) estimation is an indispensable component
    of speech spectral enhancement systems. In this paper we present a noise PSD tracking
    algorithm, which employs a noise presence probability estimate delivered by a
    deep neural network (DNN). The algorithm provides a causal noise PSD estimate
    and can thus be used in speech enhancement systems for communication purposes.
    An extensive performance comparison has been carried out with ten causal state-of-the-art
    noise tracking algorithms taken from the literature and categorized acc. to applied
    techniques. The experiments showed that the proposed DNN-based noise PSD tracker
    outperforms all competing methods with respect to all tested performance measures,
    which include the noise tracking performance and the performance of a speech enhancement
    system employing the noise tracking component.
author:
- first_name: Aleksej
  full_name: Chinaev, Aleksej
  last_name: Chinaev
- 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
citation:
  ama: 'Chinaev A, Heymann J, Drude L, Haeb-Umbach R. Noise-Presence-Probability-Based
    Noise PSD Estimation by Using DNNs. In: <i>12. ITG Fachtagung Sprachkommunikation
    (ITG 2016)</i>. ; 2016.'
  apa: Chinaev, A., Heymann, J., Drude, L., &#38; Haeb-Umbach, R. (2016). Noise-Presence-Probability-Based
    Noise PSD Estimation by Using DNNs. In <i>12. ITG Fachtagung Sprachkommunikation
    (ITG 2016)</i>.
  bibtex: '@inproceedings{Chinaev_Heymann_Drude_Haeb-Umbach_2016, title={Noise-Presence-Probability-Based
    Noise PSD Estimation by Using DNNs}, booktitle={12. ITG Fachtagung Sprachkommunikation
    (ITG 2016)}, author={Chinaev, Aleksej and Heymann, Jahn and Drude, Lukas and Haeb-Umbach,
    Reinhold}, year={2016} }'
  chicago: Chinaev, Aleksej, Jahn Heymann, Lukas Drude, and Reinhold Haeb-Umbach.
    “Noise-Presence-Probability-Based Noise PSD Estimation by Using DNNs.” In <i>12.
    ITG Fachtagung Sprachkommunikation (ITG 2016)</i>, 2016.
  ieee: A. Chinaev, J. Heymann, L. Drude, and R. Haeb-Umbach, “Noise-Presence-Probability-Based
    Noise PSD Estimation by Using DNNs,” in <i>12. ITG Fachtagung Sprachkommunikation
    (ITG 2016)</i>, 2016.
  mla: Chinaev, Aleksej, et al. “Noise-Presence-Probability-Based Noise PSD Estimation
    by Using DNNs.” <i>12. ITG Fachtagung Sprachkommunikation (ITG 2016)</i>, 2016.
  short: 'A. Chinaev, J. Heymann, L. Drude, R. Haeb-Umbach, in: 12. ITG Fachtagung
    Sprachkommunikation (ITG 2016), 2016.'
date_created: 2019-07-12T05:27:25Z
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/2016/ChHeyDrHa16.pdf
oa: '1'
publication: 12. ITG Fachtagung Sprachkommunikation (ITG 2016)
related_material:
  link:
  - description: Presentation
    relation: supplementary_material
    url: https://groups.uni-paderborn.de/nt/pubs/2016/ChHeyDrHa16_Presentation.pdf
status: public
title: Noise-Presence-Probability-Based Noise PSD Estimation by Using DNNs
type: conference
user_id: '44006'
year: '2016'
...
---
_id: '11751'
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: Reinhold
  full_name: Haeb-Umbach, Reinhold
  id: '242'
  last_name: Haeb-Umbach
citation:
  ama: 'Drude L, Boeddeker C, Haeb-Umbach R. Blind Speech Separation based on Complex
    Spherical k-Mode Clustering. In: <i>Proc. IEEE Intl. Conf. on Acoustics, Speech
    and Signal Processing (ICASSP)</i>. ; 2016.'
  apa: Drude, L., Boeddeker, C., &#38; Haeb-Umbach, R. (2016). Blind Speech Separation
    based on Complex Spherical k-Mode Clustering. In <i>Proc. IEEE Intl. Conf. on
    Acoustics, Speech and Signal Processing (ICASSP)</i>.
  bibtex: '@inproceedings{Drude_Boeddeker_Haeb-Umbach_2016, title={Blind Speech Separation
    based on Complex Spherical k-Mode Clustering}, booktitle={Proc. IEEE Intl. Conf.
    on Acoustics, Speech and Signal Processing (ICASSP)}, author={Drude, Lukas and
    Boeddeker, Christoph and Haeb-Umbach, Reinhold}, year={2016} }'
  chicago: Drude, Lukas, Christoph Boeddeker, and Reinhold Haeb-Umbach. “Blind Speech
    Separation Based on Complex Spherical K-Mode Clustering.” In <i>Proc. IEEE Intl.
    Conf. on Acoustics, Speech and Signal Processing (ICASSP)</i>, 2016.
  ieee: L. Drude, C. Boeddeker, and R. Haeb-Umbach, “Blind Speech Separation based
    on Complex Spherical k-Mode Clustering,” in <i>Proc. IEEE Intl. Conf. on Acoustics,
    Speech and Signal Processing (ICASSP)</i>, 2016.
  mla: Drude, Lukas, et al. “Blind Speech Separation Based on Complex Spherical K-Mode
    Clustering.” <i>Proc. IEEE Intl. Conf. on Acoustics, Speech and Signal Processing
    (ICASSP)</i>, 2016.
  short: 'L. Drude, C. Boeddeker, R. Haeb-Umbach, in: Proc. IEEE Intl. Conf. on Acoustics,
    Speech and Signal Processing (ICASSP), 2016.'
date_created: 2019-07-12T05:27:33Z
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/2016/icassp_2016_drude_paper.pdf
oa: '1'
publication: Proc. IEEE Intl. Conf. on Acoustics, Speech and Signal Processing (ICASSP)
related_material:
  link:
  - description: Slides
    relation: supplementary_material
    url: https://groups.uni-paderborn.de/nt/pubs/2016/icassp_2016_drude_slides.pdf
status: public
title: Blind Speech Separation based on Complex Spherical k-Mode Clustering
type: conference
user_id: '44006'
year: '2016'
...
---
_id: '11756'
abstract:
- lang: eng
  text: Although complex-valued neural networks (CVNNs) â?? networks which can operate
    with complex arithmetic â?? have been around for a while, they have not been given
    reconsideration since the breakthrough of deep network architectures. This paper
    presents a critical assessment whether the novel tool set of deep neural networks
    (DNNs) should be extended to complex-valued arithmetic. Indeed, with DNNs making
    inroads in speech enhancement tasks, the use of complex-valued input data, specifically
    the short-time Fourier transform coefficients, is an obvious consideration. In
    particular when it comes to performing tasks that heavily rely on phase information,
    such as acoustic beamforming, complex-valued algorithms are omnipresent. In this
    contribution we recapitulate backpropagation in CVNNs, develop complex-valued
    network elements, such as the split-rectified non-linearity, and compare real-
    and complex-valued networks on a beamforming task. We find that CVNNs hardly provide
    a performance gain and conclude that the effort of developing the complex-valued
    counterparts of the building blocks of modern deep or recurrent neural networks
    can hardly be justified.
author:
- first_name: Lukas
  full_name: Drude, Lukas
  id: '11213'
  last_name: Drude
- first_name: Bhiksha
  full_name: Raj, Bhiksha
  last_name: Raj
- first_name: Reinhold
  full_name: Haeb-Umbach, Reinhold
  id: '242'
  last_name: Haeb-Umbach
citation:
  ama: 'Drude L, Raj B, Haeb-Umbach R. On the appropriateness of complex-valued neural
    networks for speech enhancement. In: <i>INTERSPEECH 2016, San Francisco, USA</i>.
    ; 2016.'
  apa: Drude, L., Raj, B., &#38; Haeb-Umbach, R. (2016). On the appropriateness of
    complex-valued neural networks for speech enhancement. In <i>INTERSPEECH 2016,
    San Francisco, USA</i>.
  bibtex: '@inproceedings{Drude_Raj_Haeb-Umbach_2016, title={On the appropriateness
    of complex-valued neural networks for speech enhancement}, booktitle={INTERSPEECH
    2016, San Francisco, USA}, author={Drude, Lukas and Raj, Bhiksha and Haeb-Umbach,
    Reinhold}, year={2016} }'
  chicago: Drude, Lukas, Bhiksha Raj, and Reinhold Haeb-Umbach. “On the Appropriateness
    of Complex-Valued Neural Networks for Speech Enhancement.” In <i>INTERSPEECH 2016,
    San Francisco, USA</i>, 2016.
  ieee: L. Drude, B. Raj, and R. Haeb-Umbach, “On the appropriateness of complex-valued
    neural networks for speech enhancement,” in <i>INTERSPEECH 2016, San Francisco,
    USA</i>, 2016.
  mla: Drude, Lukas, et al. “On the Appropriateness of Complex-Valued Neural Networks
    for Speech Enhancement.” <i>INTERSPEECH 2016, San Francisco, USA</i>, 2016.
  short: 'L. Drude, B. Raj, R. Haeb-Umbach, in: INTERSPEECH 2016, San Francisco, USA,
    2016.'
date_created: 2019-07-12T05:27:39Z
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/2016/interspeech_2016_drude_paper.pdf
oa: '1'
publication: INTERSPEECH 2016, San Francisco, USA
related_material:
  link:
  - description: Poster
    relation: supplementary_material
    url: https://groups.uni-paderborn.de/nt/pubs/2016/interspeech_2016_drude_slides.pdf
status: public
title: On the appropriateness of complex-valued neural networks for speech enhancement
type: conference
user_id: '44006'
year: '2016'
...
---
_id: '11771'
abstract:
- lang: eng
  text: This paper is concerned with speech presence probability estimation employing
    an explicit model of the temporal and spectral correlations of speech. An undirected
    graphical model is introduced, based on a Factor Graph formulation. It is shown
    that this undirected model cures some of the theoretical issues of an earlier
    directed graphical model. Furthermore, we formulate a message passing inference
    scheme based on an approximate graph factorization, identify this inference scheme
    as a particular message passing schedule based on the turbo principle and suggest
    further alternative schedules. The experiments show an improved performance over
    speech presence probability estimation based on an IID assumption, and a slightly
    better performance of the turbo schedule over the alternatives.
author:
- first_name: Thomas
  full_name: Glarner, Thomas
  id: '14169'
  last_name: Glarner
- first_name: Mohammad
  full_name: Mahdi Momenzadeh, Mohammad
  last_name: Mahdi Momenzadeh
- 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: 'Glarner T, Mahdi Momenzadeh M, Drude L, Haeb-Umbach R. Factor Graph Decoding
    for Speech Presence Probability Estimation. In: <i>12. ITG Fachtagung Sprachkommunikation
    (ITG 2016)</i>. ; 2016.'
  apa: Glarner, T., Mahdi Momenzadeh, M., Drude, L., &#38; Haeb-Umbach, R. (2016).
    Factor Graph Decoding for Speech Presence Probability Estimation. In <i>12. ITG
    Fachtagung Sprachkommunikation (ITG 2016)</i>.
  bibtex: '@inproceedings{Glarner_Mahdi Momenzadeh_Drude_Haeb-Umbach_2016, title={Factor
    Graph Decoding for Speech Presence Probability Estimation}, booktitle={12. ITG
    Fachtagung Sprachkommunikation (ITG 2016)}, author={Glarner, Thomas and Mahdi
    Momenzadeh, Mohammad and Drude, Lukas and Haeb-Umbach, Reinhold}, year={2016}
    }'
  chicago: Glarner, Thomas, Mohammad Mahdi Momenzadeh, Lukas Drude, and Reinhold Haeb-Umbach.
    “Factor Graph Decoding for Speech Presence Probability Estimation.” In <i>12.
    ITG Fachtagung Sprachkommunikation (ITG 2016)</i>, 2016.
  ieee: T. Glarner, M. Mahdi Momenzadeh, L. Drude, and R. Haeb-Umbach, “Factor Graph
    Decoding for Speech Presence Probability Estimation,” in <i>12. ITG Fachtagung
    Sprachkommunikation (ITG 2016)</i>, 2016.
  mla: Glarner, Thomas, et al. “Factor Graph Decoding for Speech Presence Probability
    Estimation.” <i>12. ITG Fachtagung Sprachkommunikation (ITG 2016)</i>, 2016.
  short: 'T. Glarner, M. Mahdi Momenzadeh, L. Drude, R. Haeb-Umbach, in: 12. ITG Fachtagung
    Sprachkommunikation (ITG 2016), 2016.'
date_created: 2019-07-12T05:27:56Z
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/2016/itgspeech2016_08_Glarner.pdf
oa: '1'
publication: 12. ITG Fachtagung Sprachkommunikation (ITG 2016)
related_material:
  link:
  - description: Slides
    relation: supplementary_material
    url: https://groups.uni-paderborn.de/nt/pubs/2016/itgspeech2016_08_Glarner_slides.pdf
status: public
title: Factor Graph Decoding for Speech Presence Probability Estimation
type: conference
user_id: '44006'
year: '2016'
...
---
_id: '11812'
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
citation:
  ama: 'Heymann J, Drude L, Haeb-Umbach R. Neural Network Based Spectral Mask Estimation
    for Acoustic Beamforming. In: <i>Proc. IEEE Intl. Conf. on Acoustics, Speech and
    Signal Processing (ICASSP)</i>. ; 2016.'
  apa: Heymann, J., Drude, L., &#38; Haeb-Umbach, R. (2016). Neural Network Based
    Spectral Mask Estimation for Acoustic Beamforming. In <i>Proc. IEEE Intl. Conf.
    on Acoustics, Speech and Signal Processing (ICASSP)</i>.
  bibtex: '@inproceedings{Heymann_Drude_Haeb-Umbach_2016, title={Neural Network Based
    Spectral Mask Estimation for Acoustic Beamforming}, booktitle={Proc. IEEE Intl.
    Conf. on Acoustics, Speech and Signal Processing (ICASSP)}, author={Heymann, Jahn
    and Drude, Lukas and Haeb-Umbach, Reinhold}, year={2016} }'
  chicago: Heymann, Jahn, Lukas Drude, and Reinhold Haeb-Umbach. “Neural Network Based
    Spectral Mask Estimation for Acoustic Beamforming.” In <i>Proc. IEEE Intl. Conf.
    on Acoustics, Speech and Signal Processing (ICASSP)</i>, 2016.
  ieee: J. Heymann, L. Drude, and R. Haeb-Umbach, “Neural Network Based Spectral Mask
    Estimation for Acoustic Beamforming,” in <i>Proc. IEEE Intl. Conf. on Acoustics,
    Speech and Signal Processing (ICASSP)</i>, 2016.
  mla: Heymann, Jahn, et al. “Neural Network Based Spectral Mask Estimation for Acoustic
    Beamforming.” <i>Proc. IEEE Intl. Conf. on Acoustics, Speech and Signal Processing
    (ICASSP)</i>, 2016.
  short: 'J. Heymann, L. Drude, R. Haeb-Umbach, in: Proc. IEEE Intl. Conf. on Acoustics,
    Speech and Signal Processing (ICASSP), 2016.'
date_created: 2019-07-12T05:28:44Z
date_updated: 2022-01-06T06:51:09Z
department:
- _id: '54'
language:
- iso: eng
main_file_link:
- open_access: '1'
  url: https://groups.uni-paderborn.de/nt/pubs/2016/icassp_2016_heymann_paper.pdf
oa: '1'
publication: Proc. IEEE Intl. Conf. on Acoustics, Speech and Signal Processing (ICASSP)
related_material:
  link:
  - description: Slides
    relation: supplementary_material
    url: https://groups.uni-paderborn.de/nt/pubs/2016/icassp_2016_heymann_slides.pdf
status: public
title: Neural Network Based Spectral Mask Estimation for Acoustic Beamforming
type: conference
user_id: '44006'
year: '2016'
...
---
_id: '11829'
abstract:
- lang: eng
  text: 'This contribution investigates Direction of Arrival (DoA) estimation using
    linearly arranged microphone arrays. We are going to develop a model for the DoA
    estimation error in a reverberant scenario and show the existence of a bias, that
    is a consequence of the linear arrangement and limited field of view (FoV) bias:
    First, the limited FoV leading to a clipping of the measurements, and, second,
    the angular distribution of the signal energy of the reflections being non-uniform.
    Since both issues are a consequence of the linear arrangement of the sensors,
    the bias arises largely independent of the kind of DoA estimator. The experimental
    evaluation demonstrates the existence of the bias for a selected number of DoA
    estimation methods and proves that the prediction from the developed theoretical
    model matches the simulation results.'
author:
- first_name: Florian
  full_name: Jacob, Florian
  last_name: Jacob
- first_name: Reinhold
  full_name: Haeb-Umbach, Reinhold
  id: '242'
  last_name: Haeb-Umbach
citation:
  ama: 'Jacob F, Haeb-Umbach R. On the Bias of Direction of Arrival Estimation Using
    Linear Microphone Arrays. In: <i>12. ITG Fachtagung Sprachkommunikation (ITG 2016)</i>.
    ; 2016.'
  apa: Jacob, F., &#38; Haeb-Umbach, R. (2016). On the Bias of Direction of Arrival
    Estimation Using Linear Microphone Arrays. In <i>12. ITG Fachtagung Sprachkommunikation
    (ITG 2016)</i>.
  bibtex: '@inproceedings{Jacob_Haeb-Umbach_2016, title={On the Bias of Direction
    of Arrival Estimation Using Linear Microphone Arrays}, booktitle={12. ITG Fachtagung
    Sprachkommunikation (ITG 2016)}, author={Jacob, Florian and Haeb-Umbach, Reinhold},
    year={2016} }'
  chicago: Jacob, Florian, and Reinhold Haeb-Umbach. “On the Bias of Direction of
    Arrival Estimation Using Linear Microphone Arrays.” In <i>12. ITG Fachtagung Sprachkommunikation
    (ITG 2016)</i>, 2016.
  ieee: F. Jacob and R. Haeb-Umbach, “On the Bias of Direction of Arrival Estimation
    Using Linear Microphone Arrays,” in <i>12. ITG Fachtagung Sprachkommunikation
    (ITG 2016)</i>, 2016.
  mla: Jacob, Florian, and Reinhold Haeb-Umbach. “On the Bias of Direction of Arrival
    Estimation Using Linear Microphone Arrays.” <i>12. ITG Fachtagung Sprachkommunikation
    (ITG 2016)</i>, 2016.
  short: 'F. Jacob, R. Haeb-Umbach, in: 12. ITG Fachtagung Sprachkommunikation (ITG
    2016), 2016.'
date_created: 2019-07-12T05:29:03Z
date_updated: 2022-01-06T06:51:10Z
department:
- _id: '54'
language:
- iso: eng
main_file_link:
- open_access: '1'
  url: https://groups.uni-paderborn.de/nt/pubs/2016/JacobHaeb_ITG2016.pdf
oa: '1'
publication: 12. ITG Fachtagung Sprachkommunikation (ITG 2016)
related_material:
  link:
  - description: Poster
    relation: supplementary_material
    url: https://groups.uni-paderborn.de/nt/pubs/2016/JacobHaeb_ITG2016_poster.pdf
status: public
title: On the Bias of Direction of Arrival Estimation Using Linear Microphone Arrays
type: conference
user_id: '44006'
year: '2016'
...
---
_id: '11834'
abstract:
- lang: eng
  text: We present a system for the 4th CHiME challenge which significantly increases
    the performance for all three tracks with respect to the provided baseline system.
    The front-end uses a bi-directional Long Short-Term Memory (BLSTM)-based neural
    network to estimate signal statistics. These then steer a Generalized Eigenvalue
    beamformer. The back-end consists of a 22 layer deep Wide Residual Network and
    two extra BLSTM layers. Working on a whole utterance instead of frames allows
    us to refine Batch-Normalization. We also train our own BLSTM-based language model.
    Adding a discriminative speaker adaptation leads to further gains. The final system
    achieves a word error rate on the six channel real test data of 3.48%. For the
    two channel track we achieve 5.96% and for the one channel track 9.34%. This is
    the best reported performance on the challenge achieved by a single system, i.e.,
    a configuration, which does not combine multiple systems. At the same time, our
    system is independent of the microphone configuration. We can thus use the same
    components for all three tracks.
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
citation:
  ama: 'Heymann J, Drude L, Haeb-Umbach R. Wide Residual BLSTM Network with Discriminative
    Speaker Adaptation for Robust Speech Recognition. In: <i>Computer Speech and Language</i>.
    ; 2016.'
  apa: Heymann, J., Drude, L., &#38; Haeb-Umbach, R. (2016). Wide Residual BLSTM Network
    with Discriminative Speaker Adaptation for Robust Speech Recognition. In <i>Computer
    Speech and Language</i>.
  bibtex: '@inproceedings{Heymann_Drude_Haeb-Umbach_2016, title={Wide Residual BLSTM
    Network with Discriminative Speaker Adaptation for Robust Speech Recognition},
    booktitle={Computer Speech and Language}, author={Heymann, Jahn and Drude, Lukas
    and Haeb-Umbach, Reinhold}, year={2016} }'
  chicago: Heymann, Jahn, Lukas Drude, and Reinhold Haeb-Umbach. “Wide Residual BLSTM
    Network with Discriminative Speaker Adaptation for Robust Speech Recognition.”
    In <i>Computer Speech and Language</i>, 2016.
  ieee: J. Heymann, L. Drude, and R. Haeb-Umbach, “Wide Residual BLSTM Network with
    Discriminative Speaker Adaptation for Robust Speech Recognition,” in <i>Computer
    Speech and Language</i>, 2016.
  mla: Heymann, Jahn, et al. “Wide Residual BLSTM Network with Discriminative Speaker
    Adaptation for Robust Speech Recognition.” <i>Computer Speech and Language</i>,
    2016.
  short: 'J. Heymann, L. Drude, R. Haeb-Umbach, in: Computer Speech and Language,
    2016.'
date_created: 2019-07-12T05:29:09Z
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/2016/chime4_upbonly_paper.pdf
oa: '1'
publication: Computer Speech and Language
related_material:
  link:
  - description: Poster
    relation: supplementary_material
    url: https://groups.uni-paderborn.de/nt/pubs/2016/chime4_upbonly_poster.pdf
status: public
title: Wide Residual BLSTM Network with Discriminative Speaker Adaptation for Robust
  Speech Recognition
type: conference
user_id: '44006'
year: '2016'
...
---
_id: '11840'
author:
- first_name: Keisuke
  full_name: Kinoshita, Keisuke
  last_name: Kinoshita
- first_name: Marc
  full_name: Delcroix, Marc
  last_name: Delcroix
- first_name: Sharon
  full_name: Gannot, Sharon
  last_name: Gannot
- first_name: Emanuel A. P.
  full_name: Habets, Emanuel A. P.
  last_name: Habets
- first_name: Reinhold
  full_name: Haeb-Umbach, Reinhold
  id: '242'
  last_name: Haeb-Umbach
- first_name: Walter
  full_name: Kellermann, Walter
  last_name: Kellermann
- first_name: Volker
  full_name: Leutnant, Volker
  last_name: Leutnant
- first_name: Roland
  full_name: Maas, Roland
  last_name: Maas
- first_name: Tomohiro
  full_name: Nakatani, Tomohiro
  last_name: Nakatani
- first_name: Bhiksha
  full_name: Raj, Bhiksha
  last_name: Raj
- first_name: Armin
  full_name: Sehr, Armin
  last_name: Sehr
- first_name: Takuya
  full_name: Yoshioka, Takuya
  last_name: Yoshioka
citation:
  ama: 'Kinoshita K, Delcroix M, Gannot S, et al. A summary of the REVERB challenge:
    state-of-the-art and remaining challenges in reverberant speech processing research.
    <i>EURASIP Journal on Advances in Signal Processing</i>. 2016.'
  apa: 'Kinoshita, K., Delcroix, M., Gannot, S., Habets, E. A. P., Haeb-Umbach, R.,
    Kellermann, W., … Yoshioka, T. (2016). A summary of the REVERB challenge: state-of-the-art
    and remaining challenges in reverberant speech processing research. <i>EURASIP
    Journal on Advances in Signal Processing</i>.'
  bibtex: '@article{Kinoshita_Delcroix_Gannot_Habets_Haeb-Umbach_Kellermann_Leutnant_Maas_Nakatani_Raj_et
    al._2016, title={A summary of the REVERB challenge: state-of-the-art and remaining
    challenges in reverberant speech processing research}, journal={EURASIP Journal
    on Advances in Signal Processing}, author={Kinoshita, Keisuke and Delcroix, Marc
    and Gannot, Sharon and Habets, Emanuel A. P. and Haeb-Umbach, Reinhold and Kellermann,
    Walter and Leutnant, Volker and Maas, Roland and Nakatani, Tomohiro and Raj, Bhiksha
    and et al.}, year={2016} }'
  chicago: 'Kinoshita, Keisuke, Marc Delcroix, Sharon Gannot, Emanuel A. P. Habets,
    Reinhold Haeb-Umbach, Walter Kellermann, Volker Leutnant, et al. “A Summary of
    the REVERB Challenge: State-of-the-Art and Remaining Challenges in Reverberant
    Speech Processing Research.” <i>EURASIP Journal on Advances in Signal Processing</i>,
    2016.'
  ieee: 'K. Kinoshita <i>et al.</i>, “A summary of the REVERB challenge: state-of-the-art
    and remaining challenges in reverberant speech processing research,” <i>EURASIP
    Journal on Advances in Signal Processing</i>, 2016.'
  mla: 'Kinoshita, Keisuke, et al. “A Summary of the REVERB Challenge: State-of-the-Art
    and Remaining Challenges in Reverberant Speech Processing Research.” <i>EURASIP
    Journal on Advances in Signal Processing</i>, 2016.'
  short: K. Kinoshita, M. Delcroix, S. Gannot, E.A.P. Habets, R. Haeb-Umbach, W. Kellermann,
    V. Leutnant, R. Maas, T. Nakatani, B. Raj, A. Sehr, T. Yoshioka, EURASIP Journal
    on Advances in Signal Processing (2016).
date_created: 2019-07-12T05:29:16Z
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/2016/REVERB_summary.pdf
oa: '1'
publication: EURASIP Journal on Advances in Signal Processing
status: public
title: 'A summary of the REVERB challenge: state-of-the-art and remaining challenges
  in reverberant speech processing research'
type: journal_article
user_id: '44006'
year: '2016'
...
---
_id: '11886'
abstract:
- lang: eng
  text: Today, we are often surrounded by devices with one or more microphones, such
    as smartphones, laptops, and wireless microphones. If they are part of an acoustic
    sensor network, their distribution in the environment can be beneficially exploited
    for various speech processing tasks. However, applications like speaker localization,
    speaker tracking, and speech enhancement by beamforming avail themselves of the
    geometrical configuration of the sensors. Therefore, acoustic microphone geometry
    calibration has recently become a very active field of research. This article
    provides an application-oriented, comprehensive survey of existing methods for
    microphone position self-calibration, which will be categorized by the measurements
    they use and the scenarios they can calibrate. Selected methods will be evaluated
    comparatively with real-world recordings.
author:
- first_name: Axel
  full_name: Plinge, Axel
  last_name: Plinge
- first_name: Florian
  full_name: Jacob, Florian
  last_name: Jacob
- first_name: Reinhold
  full_name: Haeb-Umbach, Reinhold
  id: '242'
  last_name: Haeb-Umbach
- first_name: Gernot A.
  full_name: Fink, Gernot A.
  last_name: Fink
citation:
  ama: 'Plinge A, Jacob F, Haeb-Umbach R, Fink GA. Acoustic Microphone Geometry Calibration:
    An overview and experimental evaluation of state-of-the-art algorithms. <i>IEEE
    Signal Processing Magazine</i>. 2016;33(4):14-29. doi:<a href="https://doi.org/10.1109/MSP.2016.2555198">10.1109/MSP.2016.2555198</a>'
  apa: 'Plinge, A., Jacob, F., Haeb-Umbach, R., &#38; Fink, G. A. (2016). Acoustic
    Microphone Geometry Calibration: An overview and experimental evaluation of state-of-the-art
    algorithms. <i>IEEE Signal Processing Magazine</i>, <i>33</i>(4), 14–29. <a href="https://doi.org/10.1109/MSP.2016.2555198">https://doi.org/10.1109/MSP.2016.2555198</a>'
  bibtex: '@article{Plinge_Jacob_Haeb-Umbach_Fink_2016, title={Acoustic Microphone
    Geometry Calibration: An overview and experimental evaluation of state-of-the-art
    algorithms}, volume={33}, DOI={<a href="https://doi.org/10.1109/MSP.2016.2555198">10.1109/MSP.2016.2555198</a>},
    number={4}, journal={IEEE Signal Processing Magazine}, author={Plinge, Axel and
    Jacob, Florian and Haeb-Umbach, Reinhold and Fink, Gernot A.}, year={2016}, pages={14–29}
    }'
  chicago: 'Plinge, Axel, Florian Jacob, Reinhold Haeb-Umbach, and Gernot A. Fink.
    “Acoustic Microphone Geometry Calibration: An Overview and Experimental Evaluation
    of State-of-the-Art Algorithms.” <i>IEEE Signal Processing Magazine</i> 33, no.
    4 (2016): 14–29. <a href="https://doi.org/10.1109/MSP.2016.2555198">https://doi.org/10.1109/MSP.2016.2555198</a>.'
  ieee: 'A. Plinge, F. Jacob, R. Haeb-Umbach, and G. A. Fink, “Acoustic Microphone
    Geometry Calibration: An overview and experimental evaluation of state-of-the-art
    algorithms,” <i>IEEE Signal Processing Magazine</i>, vol. 33, no. 4, pp. 14–29,
    2016.'
  mla: 'Plinge, Axel, et al. “Acoustic Microphone Geometry Calibration: An Overview
    and Experimental Evaluation of State-of-the-Art Algorithms.” <i>IEEE Signal Processing
    Magazine</i>, vol. 33, no. 4, 2016, pp. 14–29, doi:<a href="https://doi.org/10.1109/MSP.2016.2555198">10.1109/MSP.2016.2555198</a>.'
  short: A. Plinge, F. Jacob, R. Haeb-Umbach, G.A. Fink, IEEE Signal Processing Magazine
    33 (2016) 14–29.
date_created: 2019-07-12T05:30:09Z
date_updated: 2022-01-06T06:51:11Z
department:
- _id: '54'
doi: 10.1109/MSP.2016.2555198
intvolume: '        33'
issue: '4'
keyword:
- Acoustic sensors
- Microphones
- Portable computers
- Smart phones
- Wireless communication
- Wireless sensor networks
language:
- iso: eng
page: 14-29
publication: IEEE Signal Processing Magazine
publication_identifier:
  issn:
  - 1053-5888
status: public
title: 'Acoustic Microphone Geometry Calibration: An overview and experimental evaluation
  of state-of-the-art algorithms'
type: journal_article
user_id: '44006'
volume: 33
year: '2016'
...
---
_id: '11908'
abstract:
- lang: eng
  text: 'This paper describes automatic speech recognition (ASR) systems developed
    jointly by RWTH, UPB and FORTH for the 1ch, 2ch and 6ch track of the 4th CHiME
    Challenge. In the 2ch and 6ch tracks the final system output is obtained by a
    Confusion Network Combination (CNC) of multiple systems. The Acoustic Model (AM)
    is a deep neural network based on Bidirectional Long Short-Term Memory (BLSTM)
    units. The systems differ by front ends and training sets used for the acoustic
    training. The model for the 1ch track is trained without any preprocessing. For
    each front end we trained and evaluated individual acoustic models. We compare
    the ASR performance of different beamforming approaches: a conventional superdirective
    beamformer [1] and an MVDR beamformer as in [2], where the steering vector is
    estimated based on [3]. Furthermore we evaluated a BLSTM supported Generalized
    Eigenvalue beamformer using NN-GEV [4]. The back end is implemented using RWTH?s
    open-source toolkits RASR [5], RETURNN [6] and rwthlm [7]. We rescore lattices
    with a Long Short-Term Memory (LSTM) based language model. The overall best results
    are obtained by a system combination that includes the lattices from the system
    of UPB?s submission [8]. Our final submission scored second in each of the three
    tracks of the 4th CHiME Challenge.'
author:
- first_name: Tobias
  full_name: Menne, Tobias
  last_name: Menne
- first_name: Jahn
  full_name: Heymann, Jahn
  id: '9168'
  last_name: Heymann
- first_name: Anastasios
  full_name: Alexandridis, Anastasios
  last_name: Alexandridis
- first_name: Kazuki
  full_name: Irie, Kazuki
  last_name: Irie
- first_name: Albert
  full_name: Zeyer, Albert
  last_name: Zeyer
- first_name: Markus
  full_name: Kitza, Markus
  last_name: Kitza
- first_name: Pavel
  full_name: Golik, Pavel
  last_name: Golik
- first_name: Ilia
  full_name: Kulikov, Ilia
  last_name: Kulikov
- first_name: Lukas
  full_name: Drude, Lukas
  id: '11213'
  last_name: Drude
- first_name: Ralf
  full_name: Schlüter, Ralf
  last_name: Schlüter
- first_name: Hermann
  full_name: Ney, Hermann
  last_name: Ney
- first_name: Reinhold
  full_name: Haeb-Umbach, Reinhold
  id: '242'
  last_name: Haeb-Umbach
- first_name: Athanasios
  full_name: Mouchtaris, Athanasios
  last_name: Mouchtaris
citation:
  ama: 'Menne T, Heymann J, Alexandridis A, et al. The RWTH/UPB/FORTH System Combination
    for the 4th CHiME Challenge Evaluation. In: <i>Computer Speech and Language</i>.
    ; 2016.'
  apa: Menne, T., Heymann, J., Alexandridis, A., Irie, K., Zeyer, A., Kitza, M., …
    Mouchtaris, A. (2016). The RWTH/UPB/FORTH System Combination for the 4th CHiME
    Challenge Evaluation. In <i>Computer Speech and Language</i>.
  bibtex: '@inproceedings{Menne_Heymann_Alexandridis_Irie_Zeyer_Kitza_Golik_Kulikov_Drude_Schlüter_et
    al._2016, title={The RWTH/UPB/FORTH System Combination for the 4th CHiME Challenge
    Evaluation}, booktitle={Computer Speech and Language}, author={Menne, Tobias and
    Heymann, Jahn and Alexandridis, Anastasios and Irie, Kazuki and Zeyer, Albert
    and Kitza, Markus and Golik, Pavel and Kulikov, Ilia and Drude, Lukas and Schlüter,
    Ralf and et al.}, year={2016} }'
  chicago: Menne, Tobias, Jahn Heymann, Anastasios Alexandridis, Kazuki Irie, Albert
    Zeyer, Markus Kitza, Pavel Golik, et al. “The RWTH/UPB/FORTH System Combination
    for the 4th CHiME Challenge Evaluation.” In <i>Computer Speech and Language</i>,
    2016.
  ieee: T. Menne <i>et al.</i>, “The RWTH/UPB/FORTH System Combination for the 4th
    CHiME Challenge Evaluation,” in <i>Computer Speech and Language</i>, 2016.
  mla: Menne, Tobias, et al. “The RWTH/UPB/FORTH System Combination for the 4th CHiME
    Challenge Evaluation.” <i>Computer Speech and Language</i>, 2016.
  short: 'T. Menne, J. Heymann, A. Alexandridis, K. Irie, A. Zeyer, M. Kitza, P. Golik,
    I. Kulikov, L. Drude, R. Schlüter, H. Ney, R. Haeb-Umbach, A. Mouchtaris, in:
    Computer Speech and Language, 2016.'
date_created: 2019-07-12T05:30:35Z
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/2016/chime4_rwthupbforth_paper.pdf
oa: '1'
publication: Computer Speech and Language
status: public
title: The RWTH/UPB/FORTH System Combination for the 4th CHiME Challenge Evaluation
type: conference
user_id: '44006'
year: '2016'
...
---
_id: '11920'
abstract:
- lang: eng
  text: In this paper we demonstrate an algorithm to learn words from speech using
    non-parametric Bayesian hierarchical models in an unsupervised setting. We exploit
    the assumption of a hierarchical structure of speech, namely the formation of
    spoken words as a sequence of phonemes. We employ the Nested Hierarchical Pitman-Yor
    Language Model, which allows an a priori unknown and possibly unlimited number
    of words. We assume the n-gram probabilities of words, the m-gram probabilities
    of phoneme sequences in words and the phoneme sequences of the words themselves
    as latent variables to be learned. We evaluate the algorithm on a cross language
    task using an existing speech recognizer trained on English speech to decode speech
    in the Xitsonga language supplied for the 2015 ZeroSpeech challenge. We apply
    the learning algorithm on the resulting phoneme graphs and achieve the highest
    token precision and F score compared to present systems.
author:
- first_name: Oliver
  full_name: Walter, Oliver
  last_name: Walter
- first_name: Reinhold
  full_name: Haeb-Umbach, Reinhold
  id: '242'
  last_name: Haeb-Umbach
citation:
  ama: 'Walter O, Haeb-Umbach R. Unsupervised Word Discovery from Speech using Bayesian
    Hierarchical Models. In: <i>38th German Conference on Pattern Recognition (GCPR
    2016)</i>. ; 2016.'
  apa: Walter, O., &#38; Haeb-Umbach, R. (2016). Unsupervised Word Discovery from
    Speech using Bayesian Hierarchical Models. In <i>38th German Conference on Pattern
    Recognition (GCPR 2016)</i>.
  bibtex: '@inproceedings{Walter_Haeb-Umbach_2016, title={Unsupervised Word Discovery
    from Speech using Bayesian Hierarchical Models}, booktitle={38th German Conference
    on Pattern Recognition (GCPR 2016)}, author={Walter, Oliver and Haeb-Umbach, Reinhold},
    year={2016} }'
  chicago: Walter, Oliver, and Reinhold Haeb-Umbach. “Unsupervised Word Discovery
    from Speech Using Bayesian Hierarchical Models.” In <i>38th German Conference
    on Pattern Recognition (GCPR 2016)</i>, 2016.
  ieee: O. Walter and R. Haeb-Umbach, “Unsupervised Word Discovery from Speech using
    Bayesian Hierarchical Models,” in <i>38th German Conference on Pattern Recognition
    (GCPR 2016)</i>, 2016.
  mla: Walter, Oliver, and Reinhold Haeb-Umbach. “Unsupervised Word Discovery from
    Speech Using Bayesian Hierarchical Models.” <i>38th German Conference on Pattern
    Recognition (GCPR 2016)</i>, 2016.
  short: 'O. Walter, R. Haeb-Umbach, in: 38th German Conference on Pattern Recognition
    (GCPR 2016), 2016.'
date_created: 2019-07-12T05:30:49Z
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/2016/WaHa16.pdf
oa: '1'
publication: 38th German Conference on Pattern Recognition (GCPR 2016)
related_material:
  link:
  - description: Presentation
    relation: supplementary_material
    url: https://groups.uni-paderborn.de/nt/pubs/2016/WaHa16_Talk.pdf
status: public
title: Unsupervised Word Discovery from Speech using Bayesian Hierarchical Models
type: conference
user_id: '44006'
year: '2016'
...
---
_id: '11890'
abstract:
- lang: eng
  text: In this paper we study the influence of directional radio patterns of Bluetooth
    low energy (BLE) beacons on smartphone localization accuracy and beacon network
    planning. A two-dimensional model of the power emission characteristic is derived
    from measurements of the radiation pattern of BLE beacons carried out in an RF
    chamber. The Cramer-Rao lower bound (CRLB) for position estimation is then derived
    for this directional power emission model. With this lower bound on the RMS positioning
    error the coverage of different beacon network configurations can be evaluated.
    For near-optimal network planing an evolutionary optimization algorithm for finding
    the best beacon placement is presented.
author:
- first_name: Joerg
  full_name: Schmalenstroeer, Joerg
  id: '460'
  last_name: Schmalenstroeer
- first_name: Reinhold
  full_name: Haeb-Umbach, Reinhold
  id: '242'
  last_name: Haeb-Umbach
citation:
  ama: 'Schmalenstroeer J, Haeb-Umbach R. Investigations into Bluetooth Low Energy
    Localization Precision Limits. In: <i>24th European Signal Processing Conference
    (EUSIPCO 2016)</i>. ; 2016.'
  apa: Schmalenstroeer, J., &#38; Haeb-Umbach, R. (2016). Investigations into Bluetooth
    Low Energy Localization Precision Limits. <i>24th European Signal Processing Conference
    (EUSIPCO 2016)</i>.
  bibtex: '@inproceedings{Schmalenstroeer_Haeb-Umbach_2016, title={Investigations
    into Bluetooth Low Energy Localization Precision Limits}, booktitle={24th European
    Signal Processing Conference (EUSIPCO 2016)}, author={Schmalenstroeer, Joerg and
    Haeb-Umbach, Reinhold}, year={2016} }'
  chicago: Schmalenstroeer, Joerg, and Reinhold Haeb-Umbach. “Investigations into
    Bluetooth Low Energy Localization Precision Limits.” In <i>24th European Signal
    Processing Conference (EUSIPCO 2016)</i>, 2016.
  ieee: J. Schmalenstroeer and R. Haeb-Umbach, “Investigations into Bluetooth Low
    Energy Localization Precision Limits,” 2016.
  mla: Schmalenstroeer, Joerg, and Reinhold Haeb-Umbach. “Investigations into Bluetooth
    Low Energy Localization Precision Limits.” <i>24th European Signal Processing
    Conference (EUSIPCO 2016)</i>, 2016.
  short: 'J. Schmalenstroeer, R. Haeb-Umbach, in: 24th European Signal Processing
    Conference (EUSIPCO 2016), 2016.'
date_created: 2019-07-12T05:30:14Z
date_updated: 2023-10-26T08:11:52Z
department:
- _id: '54'
language:
- iso: eng
main_file_link:
- open_access: '1'
  url: https://groups.uni-paderborn.de/nt/pubs/2016/SchHaeb16.pdf
oa: '1'
publication: 24th European Signal Processing Conference (EUSIPCO 2016)
quality_controlled: '1'
related_material:
  link:
  - description: Poster
    relation: supplementary_material
    url: https://groups.uni-paderborn.de/nt/pubs/2016/SchHaeb16_Poster.pdf
status: public
title: Investigations into Bluetooth Low Energy Localization Precision Limits
type: conference
user_id: '460'
year: '2016'
...
---
_id: '11739'
abstract:
- lang: eng
  text: Noise tracking is an important component of speech enhancement algorithms.
    Of the many noise trackers proposed, Minimum Statistics (MS) is a particularly
    popular one due to its simple parameterization and at the same time excellent
    performance. In this paper we propose to further reduce the number of MS parameters
    by giving an alternative derivation of an optimal smoothing constant. At the same
    time the noise tracking performance is improved as is demonstrated by experiments
    employing speech degraded by various noise types and at different SNR values.
author:
- first_name: Aleksej
  full_name: Chinaev, Aleksej
  last_name: Chinaev
- first_name: Reinhold
  full_name: Haeb-Umbach, Reinhold
  id: '242'
  last_name: Haeb-Umbach
citation:
  ama: 'Chinaev A, Haeb-Umbach R. On Optimal Smoothing in Minimum Statistics Based
    Noise Tracking. In: <i>Interspeech 2015</i>. ; 2015:1785-1789.'
  apa: Chinaev, A., &#38; Haeb-Umbach, R. (2015). On Optimal Smoothing in Minimum
    Statistics Based Noise Tracking. In <i>Interspeech 2015</i> (pp. 1785–1789).
  bibtex: '@inproceedings{Chinaev_Haeb-Umbach_2015, title={On Optimal Smoothing in
    Minimum Statistics Based Noise Tracking}, booktitle={Interspeech 2015}, author={Chinaev,
    Aleksej and Haeb-Umbach, Reinhold}, year={2015}, pages={1785–1789} }'
  chicago: Chinaev, Aleksej, and Reinhold Haeb-Umbach. “On Optimal Smoothing in Minimum
    Statistics Based Noise Tracking.” In <i>Interspeech 2015</i>, 1785–89, 2015.
  ieee: A. Chinaev and R. Haeb-Umbach, “On Optimal Smoothing in Minimum Statistics
    Based Noise Tracking,” in <i>Interspeech 2015</i>, 2015, pp. 1785–1789.
  mla: Chinaev, Aleksej, and Reinhold Haeb-Umbach. “On Optimal Smoothing in Minimum
    Statistics Based Noise Tracking.” <i>Interspeech 2015</i>, 2015, pp. 1785–89.
  short: 'A. Chinaev, R. Haeb-Umbach, in: Interspeech 2015, 2015, pp. 1785–1789.'
date_created: 2019-07-12T05:27:19Z
date_updated: 2022-01-06T06:51:08Z
department:
- _id: '54'
keyword:
- speech enhancement
- noise tracking
- optimal smoothing
language:
- iso: eng
main_file_link:
- open_access: '1'
  url: https://groups.uni-paderborn.de/nt/pubs/2015/ChHa15.pdf
oa: '1'
page: 1785-1789
publication: Interspeech 2015
related_material:
  link:
  - description: Poster
    relation: supplementary_material
    url: https://groups.uni-paderborn.de/nt/pubs/2015/ChHa15_Poster.pdf
status: public
title: On Optimal Smoothing in Minimum Statistics Based Noise Tracking
type: conference
user_id: '44006'
year: '2015'
...
---
_id: '11748'
abstract:
- lang: eng
  text: We present a semantic analysis technique for spoken input using Markov Logic
    Networks (MLNs). MLNs combine graphical models with first-order logic. They areparticularly
    suitable for providing inference in the presence of inconsistent and incomplete
    data, which are typical of an automatic speech recognizer's (ASR) output in the
    presence of degraded speech. The target application is a speech interface to a
    home automation system to be operated by people with speech impairments, where
    the ASR output is particularly noisy. In order to cater for dysarthric speech
    with non-canonical phoneme realizations, acoustic representations of the input
    speech are learned in an unsupervised fashion. While training data transcripts
    are not required for the acoustic model training, the MLN training requires supervision,
    however, at a rather loose and abstract level. Results on two databases, one of
    them for dysarthric speech, show that MLN-based semantic analysis clearly outperforms
    baseline approaches employing non-negative matrix factorization, multinomial naive
    Bayes models, or support vector machines.
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. Semantic Analysis of Spoken Input using
    Markov Logic Networks. In: <i>INTERSPEECH 2015</i>. ; 2015.'
  apa: Despotovic, V., Walter, O., &#38; Haeb-Umbach, R. (2015). Semantic Analysis
    of Spoken Input using Markov Logic Networks. In <i>INTERSPEECH 2015</i>.
  bibtex: '@inproceedings{Despotovic_Walter_Haeb-Umbach_2015, title={Semantic Analysis
    of Spoken Input using Markov Logic Networks}, booktitle={INTERSPEECH 2015}, author={Despotovic,
    Vladimir and Walter, Oliver and Haeb-Umbach, Reinhold}, year={2015} }'
  chicago: Despotovic, Vladimir, Oliver Walter, and Reinhold Haeb-Umbach. “Semantic
    Analysis of Spoken Input Using Markov Logic Networks.” In <i>INTERSPEECH 2015</i>,
    2015.
  ieee: V. Despotovic, O. Walter, and R. Haeb-Umbach, “Semantic Analysis of Spoken
    Input using Markov Logic Networks,” in <i>INTERSPEECH 2015</i>, 2015.
  mla: Despotovic, Vladimir, et al. “Semantic Analysis of Spoken Input Using Markov
    Logic Networks.” <i>INTERSPEECH 2015</i>, 2015.
  short: 'V. Despotovic, O. Walter, R. Haeb-Umbach, in: INTERSPEECH 2015, 2015.'
date_created: 2019-07-12T05:27:30Z
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/2015/DeWaHa.pdf
oa: '1'
publication: INTERSPEECH 2015
related_material:
  link:
  - description: Poster
    relation: supplementary_material
    url: https://groups.uni-paderborn.de/nt/pubs/2015/DeWaHa_Poster.pdf
status: public
title: Semantic Analysis of Spoken Input using Markov Logic Networks
type: conference
user_id: '44006'
year: '2015'
...
---
_id: '11755'
abstract:
- lang: eng
  text: This contribution presents a Direction of Arrival (DoA) estimation algorithm
    based on the complex Watson distribution to incorporate both phase and level differences
    of captured micro- phone array signals. The derived algorithm is reviewed in the
    context of the Generalized State Coherence Transform (GSCT) on the one hand and
    a kernel density estimation method on the other hand. A thorough simulative evaluation
    yields insight into parameter selection and provides details on the performance
    for both directional and omni-directional microphones. A comparison to the well
    known Steered Response Power with Phase Transform (SRP-PHAT) algorithm and a state
    of the art DoA estimator which explicitly accounts for aliasing, shows in particular
    the advantages of presented algorithm if inter-sensor level differences are indicative
    of the DoA, as with directional microphones.
author:
- first_name: Lukas
  full_name: Drude, Lukas
  id: '11213'
  last_name: Drude
- first_name: Florian
  full_name: Jacob, Florian
  last_name: Jacob
- first_name: Reinhold
  full_name: Haeb-Umbach, Reinhold
  id: '242'
  last_name: Haeb-Umbach
citation:
  ama: 'Drude L, Jacob F, Haeb-Umbach R. DOA-Estimation based on a Complex Watson
    Kernel Method. In: <i>23th European Signal Processing Conference (EUSIPCO 2015)</i>.
    ; 2015.'
  apa: Drude, L., Jacob, F., &#38; Haeb-Umbach, R. (2015). DOA-Estimation based on
    a Complex Watson Kernel Method. In <i>23th European Signal Processing Conference
    (EUSIPCO 2015)</i>.
  bibtex: '@inproceedings{Drude_Jacob_Haeb-Umbach_2015, title={DOA-Estimation based
    on a Complex Watson Kernel Method}, booktitle={23th European Signal Processing
    Conference (EUSIPCO 2015)}, author={Drude, Lukas and Jacob, Florian and Haeb-Umbach,
    Reinhold}, year={2015} }'
  chicago: Drude, Lukas, Florian Jacob, and Reinhold Haeb-Umbach. “DOA-Estimation
    Based on a Complex Watson Kernel Method.” In <i>23th European Signal Processing
    Conference (EUSIPCO 2015)</i>, 2015.
  ieee: L. Drude, F. Jacob, and R. Haeb-Umbach, “DOA-Estimation based on a Complex
    Watson Kernel Method,” in <i>23th European Signal Processing Conference (EUSIPCO
    2015)</i>, 2015.
  mla: Drude, Lukas, et al. “DOA-Estimation Based on a Complex Watson Kernel Method.”
    <i>23th European Signal Processing Conference (EUSIPCO 2015)</i>, 2015.
  short: 'L. Drude, F. Jacob, R. Haeb-Umbach, in: 23th European Signal Processing
    Conference (EUSIPCO 2015), 2015.'
date_created: 2019-07-12T05:27:38Z
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/2015/DrJaHa15.pdf
oa: '1'
publication: 23th European Signal Processing Conference (EUSIPCO 2015)
related_material:
  link:
  - description: Presentation
    relation: supplementary_material
    url: https://groups.uni-paderborn.de/nt/pubs/2015/DrJaHa15_Presentation.pdf
status: public
title: DOA-Estimation based on a Complex Watson Kernel Method
type: conference
user_id: '44006'
year: '2015'
...