---
res:
  bibo_abstract:
  - 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.@eng
  bibo_authorlist:
  - foaf_Person:
      foaf_givenName: Lukas
      foaf_name: Drude, Lukas
      foaf_surname: Drude
      foaf_workInfoHomepage: http://www.librecat.org/personId=11213
  - foaf_Person:
      foaf_givenName: Aleksej
      foaf_name: Chinaev, Aleksej
      foaf_surname: Chinaev
  - foaf_Person:
      foaf_givenName: Dang Hai
      foaf_name: Tran Vu, Dang Hai
      foaf_surname: Tran Vu
  - foaf_Person:
      foaf_givenName: Reinhold
      foaf_name: Haeb-Umbach, Reinhold
      foaf_surname: Haeb-Umbach
      foaf_workInfoHomepage: http://www.librecat.org/personId=242
  dct_date: 2014^xs_gYear
  dct_language: eng
  dct_subject:
  - Accuracy
  - Acoustics
  - Estimation
  - Mathematical model
  - Soruce separation
  - Speech
  - Vectors
  - Bayes methods
  - Blind source separation
  - Directional statistics
  - Number of speakers
  - Speaker diarization
  dct_title: Towards Online Source Counting in Speech Mixtures Applying a Variational
    EM for Complex Watson Mixture Models@
...