TY - CONF
AB - 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.
AU - Drude, Lukas
AU - Chinaev, Aleksej
AU - Tran Vu, Dang Hai
AU - Haeb-Umbach, Reinhold
ID - 11753
KW - Accuracy
KW - Acoustics
KW - Estimation
KW - Mathematical model
KW - Soruce separation
KW - Speech
KW - Vectors
KW - Bayes methods
KW - Blind source separation
KW - Directional statistics
KW - Number of speakers
KW - Speaker diarization
T2 - 14th International Workshop on Acoustic Signal Enhancement (IWAENC 2014)
TI - Towards Online Source Counting in Speech Mixtures Applying a Variational EM for Complex Watson Mixture Models
ER -