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A quick and accurate way to rotate and shift nuclear magnetic resonance (NMR) images using the two-dimensional chirp-z transform is presented. When the desired image grid is rotated and shifted from the original grid due to patient motion, the chirp-z transform can reconstruct NMR images directly onto the ultimate grid instead of reconstructing onto the(More)
In this letter, we propose a tensor factorization approach for multichannel speech enhancement, which is very successful even when the noise level is high. Specifically, we extend the well-known subspace approach to arbitrary orders and present the higher order subspace approach for multichannel speech enhancement. Unlike previous algorithms, the proposed(More)
In this paper, we propose a robust time-frequency decomposition (RTFD) model to restore audio signals degraded by sparse impulse noise mixed with small dense Gaussian noise. This kind of noise is very common especially in old-time recordings. The proposed RTFD model is based on the observation that these degraded audio signals mainly contain four parts,(More)
In this paper the problem of parallel waveform enhancement via the multi-sensor fusion technology is carefully studied. Through representing the observed multiple noisy observations as a 3-D tensor, we propose two novel approaches in the time domain, i.e. the transforming and filtering (TAF) approach and the direct multidimensional filtering (DMF) approach,(More)
Recently, researchers have proposed to represent the observed multichannel speech data as a 3-D tensor and then directly reduce the noise level in the time domain. For example, a higher order subspace algorithm (HOSA) was proposed for the reduction of spatially white noise (i.e., the noise added to different sensors is mutually uncorrelated) and yielded an(More)
Abstract—In this paper, a bilinear Wiener filtering method is proposed for multichannel audio acquisition without knowing the array configurations and frequency responses. Compared with mainstream algorithms, the proposed method has two important features: blindness and robustness. For the first feature, the method does not require any prior knowledge about(More)
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