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This work proposes a solution to the problem of under-determined audio source separation using pre-trained redundant source-based prior information. In local Gaussian modeling of a mixing process, an observed mixture is modeled by a Gaussian distribution parameterized by source variances and spatial covariance matrices. The separation is performed by estimating the parameters, and applying Wiener...
In audio source separation, reference guided approaches are a class of methods that use reference signals to guide the separation. In prior work, we proposed a general framework to model the deformation between the sources and the references. In this paper, we investigate a specific scenario within this framework: music separation guided by the multitrack recording of a cover interpretation of the...
Due to a large number of parameters in deep neural networks (DNNs), it is challenging to design a small-footprint DNN-based speech recognition system while maintaining a high recognition performance. Even with a singular value matrix decomposition (SVD) method and scalar quantization, the DNN model is still too large to be deployed on many mobile devices. Common practices like reducing the number...
High spatial resolution in functional magnetic resonance imaging improves its sensitivity to brain activation signals by reducing partial volume effects. However, the long acquisition times required for high spatial resolution limit the temporal resolution in fMRI studies. Consequently, the low temporal sampling bandwidth leads to increase in physiological noise and poor modeling of the functional...
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