We address the convolutive blind source separation problem for the (over-)determined case where (i) the number of nonstationary target-sources $K$ is less than that of microphones $M$, and (ii) there are up to $M - K$ stationary Gaussian noises that need not to be extracted. Independent vector analysis (IVA) can solve the problem by separating into $M$ sources and selecting the top $K$ highly nonstationary signals among them, but this approach suffers from a waste of computation especially when $K \ll M$. Channel reductions in preprocessing of IVA by, e.g., principle component analysis have the risk of removing the target signals. We here extend IVA to resolve these issues. One such extension has been attained by assuming the orthogonality constraint (OC) that the sample correlation between the target and noise signals is to be zero. The proposed IVA, on the other hand, does not rely on OC and exploits only the independence between sources and the stationarity of the noises. This enables us to develop several efficient algorithms based on block coordinate descent methods with a problem specific acceleration. We clarify that one such algorithm exactly coincides with the conventional IVA with OC, and also explain that the other newly developed algorithms are faster than it. Experimental results show the improved computational load of the new algorithms compared to the conventional methods. In particular, a new algorithm specialized for $K = 1$ outperforms the others.
Robust Online Overdetermined Independent Vector Analysis Based on Bilinear Decomposition
School of Electronic Information, Wuhan University（武汉大学电子信息学院）；CIAIC and Shaanxi Provincial Key Laboratory of Artificial Intelligence（中国人工智能研究院和陕西省人工智能重点实验室）；Northwestern Polytechnical University（西北工业大学）；Fraunhofer IIS（弗劳恩霍夫研究所）；Technical University of Liberec（利贝雷茨技术大学）；INRS-EMT, University of Quebec（INRS-EMT，魁北克大学）；Waseda University（早稻田大学）
Gaussian speaker embedding learning for text-independent speaker verification
National Engineering Laboratory for Speech and Language Information Processing（国家语音语言信息处理工程实验室）；University of Science and Technology of China（中国科学技术大学）
Independent Vector Extraction Constrained on Manifold of Half-Length Filters
Technical University of Liberec（利贝雷茨技术大学）；University of Maryland, Baltimore County（马里兰大学巴尔的摩分校）；Naval Surface Warfare Center Carderock Division（卡德洛克海军水面作战中心）
An Adaptive X-vector Model for Text-independent Speaker Verification
National Engineering Laboratory for Speech and Language Information Processing, University of Science and Technology of China（中国科学技术大学语音与语言信息处理国家工程实验室）
