---
_id: '11753'
abstract:
- lang: eng
  text: This contribution describes a step-wise source counting algorithm to determine
    the number of speakers in an offline scenario. Each speaker is identified by a
    variational expectation maximization (VEM) algorithm for complex Watson mixture
    models and therefore directly yields beamforming vectors for a subsequent speech
    separation process. An observation selection criterion is proposed which improves
    the robustness of the source counting in noise. The algorithm is compared to an
    alternative VEM approach with Gaussian mixture models based on directions of arrival
    and shown to deliver improved source counting accuracy. The article concludes
    by extending the offline algorithm towards a low-latency online estimation of
    the number of active sources from the streaming input data.
author:
- first_name: Lukas
  full_name: Drude, Lukas
  id: '11213'
  last_name: Drude
- first_name: Aleksej
  full_name: Chinaev, Aleksej
  last_name: Chinaev
- first_name: Dang Hai
  full_name: Tran Vu, Dang Hai
  last_name: Tran Vu
- first_name: Reinhold
  full_name: Haeb-Umbach, Reinhold
  id: '242'
  last_name: Haeb-Umbach
citation:
  ama: 'Drude L, Chinaev A, Tran Vu DH, Haeb-Umbach R. Towards Online Source Counting
    in Speech Mixtures Applying a Variational EM for Complex Watson Mixture Models.
    In: <i>14th International Workshop on Acoustic Signal Enhancement (IWAENC 2014)</i>.
    ; 2014:213-217.'
  apa: Drude, L., Chinaev, A., Tran Vu, D. H., &#38; Haeb-Umbach, R. (2014). Towards
    Online Source Counting in Speech Mixtures Applying a Variational EM for Complex
    Watson Mixture Models. In <i>14th International Workshop on Acoustic Signal Enhancement
    (IWAENC 2014)</i> (pp. 213–217).
  bibtex: '@inproceedings{Drude_Chinaev_Tran Vu_Haeb-Umbach_2014, title={Towards Online
    Source Counting in Speech Mixtures Applying a Variational EM for Complex Watson
    Mixture Models}, booktitle={14th International Workshop on Acoustic Signal Enhancement
    (IWAENC 2014)}, author={Drude, Lukas and Chinaev, Aleksej and Tran Vu, Dang Hai
    and Haeb-Umbach, Reinhold}, year={2014}, pages={213–217} }'
  chicago: Drude, Lukas, Aleksej Chinaev, Dang Hai Tran Vu, and Reinhold Haeb-Umbach.
    “Towards Online Source Counting in Speech Mixtures Applying a Variational EM for
    Complex Watson Mixture Models.” In <i>14th International Workshop on Acoustic
    Signal Enhancement (IWAENC 2014)</i>, 213–17, 2014.
  ieee: L. Drude, A. Chinaev, D. H. Tran Vu, and R. Haeb-Umbach, “Towards Online Source
    Counting in Speech Mixtures Applying a Variational EM for Complex Watson Mixture
    Models,” in <i>14th International Workshop on Acoustic Signal Enhancement (IWAENC
    2014)</i>, 2014, pp. 213–217.
  mla: Drude, Lukas, et al. “Towards Online Source Counting in Speech Mixtures Applying
    a Variational EM for Complex Watson Mixture Models.” <i>14th International Workshop
    on Acoustic Signal Enhancement (IWAENC 2014)</i>, 2014, pp. 213–17.
  short: 'L. Drude, A. Chinaev, D.H. Tran Vu, R. Haeb-Umbach, in: 14th International
    Workshop on Acoustic Signal Enhancement (IWAENC 2014), 2014, pp. 213–217.'
date_created: 2019-07-12T05:27:35Z
date_updated: 2022-01-06T06:51:08Z
department:
- _id: '54'
keyword:
- Accuracy
- Acoustics
- Estimation
- Mathematical model
- Soruce separation
- Speech
- Vectors
- Bayes methods
- Blind source separation
- Directional statistics
- Number of speakers
- Speaker diarization
language:
- iso: eng
main_file_link:
- open_access: '1'
  url: https://groups.uni-paderborn.de/nt/pubs/2014/DrChTrHaeb14.pdf
oa: '1'
page: 213-217
publication: 14th International Workshop on Acoustic Signal Enhancement (IWAENC 2014)
related_material:
  link:
  - description: Poster
    relation: supplementary_material
    url: https://groups.uni-paderborn.de/nt/pubs/2014/DrChTrHaeb14_Poster.pdf
status: public
title: Towards Online Source Counting in Speech Mixtures Applying a Variational EM
  for Complex Watson Mixture Models
type: conference
user_id: '44006'
year: '2014'
...