---
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@
...