Sentence examples for a sparse source from inspiring English sources
A sparse source has a peaky probability density function; the signal is close to zero at most time-frequency points, and has large values in rare occasions.
Although positive reiteracy may be due to stable spatial patterns and significant segregation to a sparse source recruitment, both R t) and S t) may be substantially affected by frequency modulations slower than the sampling frequency.
Consider the model x=A·s; we need a linear transformation that applies to both sides of the equation to yield a new sparse source vector.
They have also proposed another method [11] for convolutive AVSS based on developing a VVAD and using it in a geometric separation algorithm using sparse source assumption.
We can see from the figure that the non-sparse source (p=0.5) has a higher saturation rate than sparse source (p=0.1). Figure 2 Information-rate bounds of compressive cooperation for sources at different sparsities.
The common structure property of the sparse source vectors suggests the application of a group sparse reconstruction.
The effect of reverberation in sparse source localization was studied by the authors in a previous work [30].
The first step of Table 16 shows a linear model for the SCA problem, the second step consists of estimating the mixing matrix A using sparsity information, and finally the third step is to estimate the sparse source representation based on the estimate of A[17].
On the other hand, the minimum description length (MDL) method, used in DOA/MSL and spectral estimation, determines the number of sparse source locations or frequencies.
By doing so, we ensure that if any enhancement is achieved in the sparsity level of residuals, it also corresponds to the physical sparse source of excitation.
The advantage of combining EEG and MEG data was also evaluated using other inverse operators, such as sparse source reconstruction (Ding and Yuan 2013) on simulated data, linearly constrained minimum variance beamformer approach on simulated and auditory data (Hong et al. 2013) or Multiple Sparse Prior methods on face evoked responses (Henson et al. 2009).
