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Author Drude, Lukas ♦ Chinaev, Aleksej ♦ Hai, Dang ♦ Vu, Tran ♦ Haeb-Umbach, Reinhold
Source CiteSeerX
Content type Text
File Format PDF
Language English
Subject Domain (in DDC) Computer science, information & general works ♦ Data processing & computer science
Description This contribution describes a step-wise source counting algo-rithm to determine the number of speakers in an offline sce-nario. 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 se-lection 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. Index Terms — Blind source separation, Bayes methods, Directional statistics, Number of speakers, Speaker diariza-tion
Educational Role Student ♦ Teacher
Age Range above 22 year
Educational Use Research
Education Level UG and PG ♦ Career/Technical Study
Learning Resource Type Article
Publisher Date 2014-01-01
Publisher Institution in International Workshop on Acoustic Signal Enhancement (IWAENC), Juan Les Pins