---
_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: <i>DCASE2019
    Workshop, New York, USA</i>. ; 2019.'
  apa: Ebbers, J., &#38; Haeb-Umbach, R. (2019). Convolutional Recurrent Neural Network
    and Data Augmentation for Audio Tagging with Noisy Labels and Minimal Supervision.
    <i>DCASE2019 Workshop, New York, USA</i>.
  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 <i>DCASE2019 Workshop, New York, USA</i>, 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.”
    <i>DCASE2019 Workshop, New York, USA</i>, 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: <i>CAMSAP 2019, Guadeloupe, West Indies</i>. ; 2019.'
  apa: Ebbers, J., Drude, L., Haeb-Umbach, R., Brendel, A., &#38; Kellermann, W. (2019).
    Weakly Supervised Sound Activity Detection and Event Classification in Acoustic
    Sensor Networks. <i>CAMSAP 2019, Guadeloupe, West Indies</i>.
  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 <i>CAMSAP 2019, Guadeloupe, West Indies</i>,
    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.” <i>CAMSAP 2019, Guadeloupe, West
    Indies</i>, 2019.
  short: 'J. Ebbers, L. Drude, R. Haeb-Umbach, A. Brendel, W. Kellermann, in: CAMSAP
    2019, Guadeloupe, West Indies, 2019.'
date_created: 2020-02-05T10:20:17Z
date_updated: 2023-11-22T08:29:58Z
ddc:
- '000'
department:
- _id: '54'
file:
- access_level: open_access
  content_type: application/pdf
  creator: huesera
  date_created: 2020-02-05T10:21:39Z
  date_updated: 2020-02-05T10:21:39Z
  file_id: '15797'
  file_name: CAMSAP_2019_WS_Ebbers_Paper.pdf
  file_size: 311887
  relation: main_file
file_date_updated: 2020-02-05T10:21:39Z
has_accepted_license: '1'
language:
- iso: eng
oa: '1'
project:
- _id: '52'
  name: Computing Resources Provided by the Paderborn Center for Parallel Computing
publication: CAMSAP 2019, Guadeloupe, West Indies
quality_controlled: '1'
status: public
title: Weakly Supervised Sound Activity Detection and Event Classification in Acoustic
  Sensor Networks
type: conference
user_id: '34851'
year: '2019'
...
---
_id: '15792'
abstract:
- lang: eng
  text: In this paper we highlight the privacy risks entailed in deep neural network
    feature extraction for domestic activity monitoring. We employ the baseline system
    proposed in the Task 5 of the DCASE 2018 challenge and simulate a feature interception
    attack by an eavesdropper who wants to perform speaker identification. We then
    propose to reduce the aforementioned privacy risks by introducing a variational
    information feature extraction scheme that allows for good activity monitoring
    performance while at the same time minimizing the information of the feature representation,
    thus restricting speaker identification attempts. We analyze the resulting model’s
    composite loss function and the budget scaling factor used to control the balance
    between the performance of the trusted and attacker tasks. It is empirically demonstrated
    that the proposed method reduces speaker identification privacy risks without
    significantly deprecating the performance of domestic activity monitoring tasks.
author:
- first_name: Alexandru
  full_name: Nelus, Alexandru
  last_name: Nelus
- first_name: Janek
  full_name: Ebbers, Janek
  id: '34851'
  last_name: Ebbers
- first_name: Reinhold
  full_name: Haeb-Umbach, Reinhold
  id: '242'
  last_name: Haeb-Umbach
- first_name: Rainer
  full_name: Martin, Rainer
  last_name: Martin
citation:
  ama: 'Nelus A, Ebbers J, Haeb-Umbach R, Martin R. Privacy-preserving Variational
    Information Feature Extraction for Domestic Activity Monitoring Versus Speaker
    Identification. In: <i>INTERSPEECH 2019, Graz, Austria</i>. ; 2019.'
  apa: Nelus, A., Ebbers, J., Haeb-Umbach, R., &#38; Martin, R. (2019). Privacy-preserving
    Variational Information Feature Extraction for Domestic Activity Monitoring Versus
    Speaker Identification. <i>INTERSPEECH 2019, Graz, Austria</i>.
  bibtex: '@inproceedings{Nelus_Ebbers_Haeb-Umbach_Martin_2019, title={Privacy-preserving
    Variational Information Feature Extraction for Domestic Activity Monitoring Versus
    Speaker Identification}, booktitle={INTERSPEECH 2019, Graz, Austria}, author={Nelus,
    Alexandru and Ebbers, Janek and Haeb-Umbach, Reinhold and Martin, Rainer}, year={2019}
    }'
  chicago: Nelus, Alexandru, Janek Ebbers, Reinhold Haeb-Umbach, and Rainer Martin.
    “Privacy-Preserving Variational Information Feature Extraction for Domestic Activity
    Monitoring Versus Speaker Identification.” In <i>INTERSPEECH 2019, Graz, Austria</i>,
    2019.
  ieee: A. Nelus, J. Ebbers, R. Haeb-Umbach, and R. Martin, “Privacy-preserving Variational
    Information Feature Extraction for Domestic Activity Monitoring Versus Speaker
    Identification,” 2019.
  mla: Nelus, Alexandru, et al. “Privacy-Preserving Variational Information Feature
    Extraction for Domestic Activity Monitoring Versus Speaker Identification.” <i>INTERSPEECH
    2019, Graz, Austria</i>, 2019.
  short: 'A. Nelus, J. Ebbers, R. Haeb-Umbach, R. Martin, in: INTERSPEECH 2019, Graz,
    Austria, 2019.'
date_created: 2020-02-05T10:07:53Z
date_updated: 2023-11-22T08:27:55Z
ddc:
- '000'
department:
- _id: '54'
file:
- access_level: open_access
  content_type: application/pdf
  creator: huesera
  date_created: 2020-02-05T10:11:40Z
  date_updated: 2020-02-05T10:11:40Z
  file_id: '15793'
  file_name: INTERSPEECH_2019_Ebbers_Paper.pdf
  file_size: 454600
  relation: main_file
file_date_updated: 2020-02-05T10:11:40Z
has_accepted_license: '1'
language:
- iso: eng
oa: '1'
publication: INTERSPEECH 2019, Graz, Austria
quality_controlled: '1'
status: public
title: Privacy-preserving Variational Information Feature Extraction for Domestic
  Activity Monitoring Versus Speaker Identification
type: conference
user_id: '34851'
year: '2019'
...
---
_id: '11760'
abstract:
- lang: eng
  text: Acoustic event detection, i.e., the task of assigning a human interpretable
    label to a segment of audio, has only recently attracted increased interest in
    the research community. Driven by the DCASE challenges and the availability of
    large-scale audio datasets, the state-of-the-art has progressed rapidly with deep-learning-based
    classi- fiers dominating the field. Because several potential use cases favor
    a realization on distributed sensor nodes, e.g. ambient assisted living applications,
    habitat monitoring or surveillance, we are concerned with two issues here. Firstly
    the classification performance of such systems and secondly the computing resources
    required to achieve a certain performance considering node level feature extraction.
    In this contribution we look at the balance between the two criteria by employing
    traditional techniques and different deep learning architectures, including convolutional
    and recurrent models in the context of real life everyday audio recordings in
    realistic, however challenging, multisource conditions.
author:
- first_name: Janek
  full_name: Ebbers, Janek
  id: '34851'
  last_name: Ebbers
- first_name: Alexandru
  full_name: Nelus, Alexandru
  last_name: Nelus
- first_name: Rainer
  full_name: Martin, Rainer
  last_name: Martin
- first_name: Reinhold
  full_name: Haeb-Umbach, Reinhold
  id: '242'
  last_name: Haeb-Umbach
citation:
  ama: 'Ebbers J, Nelus A, Martin R, Haeb-Umbach R. Evaluation of Modulation-MFCC
    Features and DNN Classification for Acoustic Event Detection. In: <i>DAGA 2018,
    München</i>. ; 2018.'
  apa: Ebbers, J., Nelus, A., Martin, R., &#38; Haeb-Umbach, R. (2018). Evaluation
    of Modulation-MFCC Features and DNN Classification for Acoustic Event Detection.
    In <i>DAGA 2018, München</i>.
  bibtex: '@inproceedings{Ebbers_Nelus_Martin_Haeb-Umbach_2018, title={Evaluation
    of Modulation-MFCC Features and DNN Classification for Acoustic Event Detection},
    booktitle={DAGA 2018, München}, author={Ebbers, Janek and Nelus, Alexandru and
    Martin, Rainer and Haeb-Umbach, Reinhold}, year={2018} }'
  chicago: Ebbers, Janek, Alexandru Nelus, Rainer Martin, and Reinhold Haeb-Umbach.
    “Evaluation of Modulation-MFCC Features and DNN Classification for Acoustic Event
    Detection.” In <i>DAGA 2018, München</i>, 2018.
  ieee: J. Ebbers, A. Nelus, R. Martin, and R. Haeb-Umbach, “Evaluation of Modulation-MFCC
    Features and DNN Classification for Acoustic Event Detection,” in <i>DAGA 2018,
    München</i>, 2018.
  mla: Ebbers, Janek, et al. “Evaluation of Modulation-MFCC Features and DNN Classification
    for Acoustic Event Detection.” <i>DAGA 2018, München</i>, 2018.
  short: 'J. Ebbers, A. Nelus, R. Martin, R. Haeb-Umbach, in: DAGA 2018, München,
    2018.'
date_created: 2019-07-12T05:27:43Z
date_updated: 2022-01-06T06:51:08Z
department:
- _id: '54'
language:
- iso: eng
main_file_link:
- open_access: '1'
  url: https://groups.uni-paderborn.de/nt/pubs/2018/Daga_2018_Ebbers_Paper.pdf
oa: '1'
publication: DAGA 2018, München
status: public
title: Evaluation of Modulation-MFCC Features and DNN Classification for Acoustic
  Event Detection
type: conference
user_id: '44006'
year: '2018'
...
---
_id: '11907'
abstract:
- lang: eng
  text: The invention of the Variational Autoencoder enables the application of Neural
    Networks to a wide range of tasks in unsupervised learning, including the field
    of Acoustic Unit Discovery (AUD). The recently proposed Hidden Markov Model Variational
    Autoencoder (HMMVAE) allows a joint training of a neural network based feature
    extractor and a structured prior for the latent space given by a Hidden Markov
    Model. It has been shown that the HMMVAE significantly outperforms pure GMM-HMM
    based systems on the AUD task. However, the HMMVAE cannot autonomously infer the
    number of acoustic units and thus relies on the GMM-HMM system for initialization.
    This paper introduces the Bayesian Hidden Markov Model Variational Autoencoder
    (BHMMVAE) which solves these issues by embedding the HMMVAE in a Bayesian framework
    with a Dirichlet Process Prior for the distribution of the acoustic units, and
    diagonal or full-covariance Gaussians as emission distributions. Experiments on
    TIMIT and Xitsonga show that the BHMMVAE is able to autonomously infer a reasonable
    number of acoustic units, can be initialized without supervision by a GMM-HMM
    system, achieves computationally efficient stochastic variational inference by
    using natural gradient descent, and, additionally, improves the AUD performance
    over the HMMVAE.
author:
- first_name: Thomas
  full_name: Glarner, Thomas
  id: '14169'
  last_name: Glarner
- first_name: Patrick
  full_name: Hanebrink, Patrick
  last_name: Hanebrink
- first_name: Janek
  full_name: Ebbers, Janek
  id: '34851'
  last_name: Ebbers
- first_name: Reinhold
  full_name: Haeb-Umbach, Reinhold
  id: '242'
  last_name: Haeb-Umbach
citation:
  ama: 'Glarner T, Hanebrink P, Ebbers J, Haeb-Umbach R. Full Bayesian Hidden Markov
    Model Variational Autoencoder for Acoustic Unit Discovery. In: <i>INTERSPEECH
    2018, Hyderabad, India</i>. ; 2018.'
  apa: Glarner, T., Hanebrink, P., Ebbers, J., &#38; Haeb-Umbach, R. (2018). Full
    Bayesian Hidden Markov Model Variational Autoencoder for Acoustic Unit Discovery.
    <i>INTERSPEECH 2018, Hyderabad, India</i>.
  bibtex: '@inproceedings{Glarner_Hanebrink_Ebbers_Haeb-Umbach_2018, title={Full Bayesian
    Hidden Markov Model Variational Autoencoder for Acoustic Unit Discovery}, booktitle={INTERSPEECH
    2018, Hyderabad, India}, author={Glarner, Thomas and Hanebrink, Patrick and Ebbers,
    Janek and Haeb-Umbach, Reinhold}, year={2018} }'
  chicago: Glarner, Thomas, Patrick Hanebrink, Janek Ebbers, and Reinhold Haeb-Umbach.
    “Full Bayesian Hidden Markov Model Variational Autoencoder for Acoustic Unit Discovery.”
    In <i>INTERSPEECH 2018, Hyderabad, India</i>, 2018.
  ieee: T. Glarner, P. Hanebrink, J. Ebbers, and R. Haeb-Umbach, “Full Bayesian Hidden
    Markov Model Variational Autoencoder for Acoustic Unit Discovery,” 2018.
  mla: Glarner, Thomas, et al. “Full Bayesian Hidden Markov Model Variational Autoencoder
    for Acoustic Unit Discovery.” <i>INTERSPEECH 2018, Hyderabad, India</i>, 2018.
  short: 'T. Glarner, P. Hanebrink, J. Ebbers, R. Haeb-Umbach, in: INTERSPEECH 2018,
    Hyderabad, India, 2018.'
date_created: 2019-07-12T05:30:34Z
date_updated: 2023-11-22T08:29:22Z
department:
- _id: '54'
language:
- iso: eng
main_file_link:
- open_access: '1'
  url: https://groups.uni-paderborn.de/nt/pubs/2018/INTERSPEECH_2018_Glarner_Paper.pdf
oa: '1'
publication: INTERSPEECH 2018, Hyderabad, India
quality_controlled: '1'
related_material:
  link:
  - description: Slides
    relation: supplementary_material
    url: https://groups.uni-paderborn.de/nt/pubs/2018/INTERSPEECH_2018_Glarner_Slides.pdf
status: public
title: Full Bayesian Hidden Markov Model Variational Autoencoder for Acoustic Unit
  Discovery
type: conference
user_id: '34851'
year: '2018'
...
---
_id: '11836'
abstract:
- lang: eng
  text: Due to their distributed nature wireless acoustic sensor networks offer great
    potential for improved signal acquisition, processing and classification for applications
    such as monitoring and surveillance, home automation, or hands-free telecommunication.
    To reduce the communication demand with a central server and to raise the privacy
    level it is desirable to perform processing at node level. The limited processing
    and memory capabilities on a sensor node, however, stand in contrast to the compute
    and memory intensive deep learning algorithms used in modern speech and audio
    processing. In this work, we perform benchmarking of commonly used convolutional
    and recurrent neural network architectures on a Raspberry Pi based acoustic sensor
    node. We show that it is possible to run medium-sized neural network topologies
    used for speech enhancement and speech recognition in real time. For acoustic
    event recognition, where predictions in a lower temporal resolution are sufficient,
    it is even possible to run current state-of-the-art deep convolutional models
    with a real-time-factor of 0:11.
author:
- first_name: Janek
  full_name: Ebbers, Janek
  id: '34851'
  last_name: Ebbers
- first_name: Jens
  full_name: Heitkaemper, Jens
  id: '27643'
  last_name: Heitkaemper
- first_name: Joerg
  full_name: Schmalenstroeer, Joerg
  id: '460'
  last_name: Schmalenstroeer
- first_name: Reinhold
  full_name: Haeb-Umbach, Reinhold
  id: '242'
  last_name: Haeb-Umbach
citation:
  ama: 'Ebbers J, Heitkaemper J, Schmalenstroeer J, Haeb-Umbach R. Benchmarking Neural
    Network Architectures for Acoustic Sensor Networks. In: <i>ITG 2018, Oldenburg,
    Germany</i>. ; 2018.'
  apa: Ebbers, J., Heitkaemper, J., Schmalenstroeer, J., &#38; Haeb-Umbach, R. (2018).
    Benchmarking Neural Network Architectures for Acoustic Sensor Networks. <i>ITG
    2018, Oldenburg, Germany</i>.
  bibtex: '@inproceedings{Ebbers_Heitkaemper_Schmalenstroeer_Haeb-Umbach_2018, title={Benchmarking
    Neural Network Architectures for Acoustic Sensor Networks}, booktitle={ITG 2018,
    Oldenburg, Germany}, author={Ebbers, Janek and Heitkaemper, Jens and Schmalenstroeer,
    Joerg and Haeb-Umbach, Reinhold}, year={2018} }'
  chicago: Ebbers, Janek, Jens Heitkaemper, Joerg Schmalenstroeer, and Reinhold Haeb-Umbach.
    “Benchmarking Neural Network Architectures for Acoustic Sensor Networks.” In <i>ITG
    2018, Oldenburg, Germany</i>, 2018.
  ieee: J. Ebbers, J. Heitkaemper, J. Schmalenstroeer, and R. Haeb-Umbach, “Benchmarking
    Neural Network Architectures for Acoustic Sensor Networks,” 2018.
  mla: Ebbers, Janek, et al. “Benchmarking Neural Network Architectures for Acoustic
    Sensor Networks.” <i>ITG 2018, Oldenburg, Germany</i>, 2018.
  short: 'J. Ebbers, J. Heitkaemper, J. Schmalenstroeer, R. Haeb-Umbach, in: ITG 2018,
    Oldenburg, Germany, 2018.'
date_created: 2019-07-12T05:29:11Z
date_updated: 2023-10-26T08:12:40Z
department:
- _id: '54'
language:
- iso: eng
main_file_link:
- open_access: '1'
  url: https://groups.uni-paderborn.de/nt/pubs/2018/ITG_2018_Ebbers_Paper.pdf
oa: '1'
publication: ITG 2018, Oldenburg, Germany
quality_controlled: '1'
related_material:
  link:
  - description: Poster
    relation: supplementary_material
    url: https://groups.uni-paderborn.de/nt/pubs/2018/ITG_2018_Ebbers_Poster.pdf
status: public
title: Benchmarking Neural Network Architectures for Acoustic Sensor Networks
type: conference
user_id: '460'
year: '2018'
...
---
_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'
...