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For the task of monaural speech enhancement, A version of Sparse Nonnegative matrix factorization (Sparse NMF) using improved Alternating Direction Method of Multipliers (IADMM) with generalized Kullback-Leibler divergence is proposed. In this paper, an alternating direction method of multipliers (ADMM) for NMF is studied, which deals with the NMF problem using the cost function of beta divergence...
The cyclic autocorrelation (CA), which only exist nonzero coefficients at the specific cycle frequencies, can exploit inherent cyclostationary properties which vary periodically in most man-made signals. However, the CA cannot usually be sparsely represented by the finite dictionary since the true cycle frequencies do not actually fall onto the discrete grid induced by the dictionary. Inspired by...
Monaural speech enhancement is a key yet challenging problem for many important real world applications. Recently, deep neural networks(DNNs)-based speech enhancement methods, which extract useful feature from complex feature, have demonstrated remarkable performance improvement. In this paper, we present a novel DNN architecture for monaural speech enhancement. Taking into account the masking properties...
Recently many saliency detection algorithms tend to employ some priors, like center prior and boundary prior, which will bring some errors hard to decrease during the follow-up works. Salient object regions are considered as the regions with uniqueness, compactness and tiny boundary connectivity. Based on this idea, a new saliency region detection is proposed. In this paper, we adopt boundary connectivity...
In order to exploit the inherent cyclostationary properties which vary periodically in most man-made signals, one prerequisite is the knowledge of the signal's cyclic autocorrelation (CA) which can be estimated from finite time-domain samples. In this paper we concern about the sparse, periodic CA estimation and focus on recovering the CA using compressive sampling, i.e. a small amount of time-domain...
In order to exploit the inherent cyclostationary properties which vary periodically in most man-made signals, one prerequisite is the knowledge of the signal's cyclic autocorrelation (CA) which can be estimated from finite time-domain samples. In this paper we concern about the sparse, periodic CA estimation and focus on recovering the CA using compressive sampling, i.e. a small amount of time-domain...
Block sparse signal recovery methods have attracted great interests which take the block structure of the nonzero coefficients into account when clustering. Compared with traditional compressive sensing methods, it can obtain better recovery performance with fewer measurements by utilizing the block-sparsity explicitly. In this paper we propose a segmented-version of the block orthogonal matching...
Spectrum Sensing is a cornerstone in cognitive radio which can detect the spectrum holes in order to raise spectrum utilization ratio. Traditional spectrum sensing detectors depend on some prior information or are restricted by low signal-to-noise ratio and computation complexity in practical application. A GoDec based spectrum sensing detector is proposed by combining covariance based method with...
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