Sentence examples for temporal sparsity from inspiring English sources

Fig. 5 Gini indices for a 10-s segment from the soundtrack of a five-channel movie signal; (a) spectral sparsity in each channel and joint spectro-spatial sparsity, (b) temporal sparsity in each channel and joint spatio-temporal sparsity, (c) spatial sparsity in each subband and frame, (d) joint spectro-temporal sparsity in each channel and joint spectro-spatio-temporal sparsity.

Next to the spatial sparsity, also, the temporal sparsity can be exploited in the state estimation [14].

This scheme jointly exploits the spectral, spatial and temporal sparsity in the input signals but typically results in some sub-filters having no taps updated.

From these spectrograms, we first analyze the sparsity across subbands (spectral sparsity), across frames (temporal sparsity) and across channels (spatial sparsity).

It can be observed that the multichannel reference spectra displays a fairly high amount of sparsity across all the three dimensions individually, with Gini indices on average above 0.5 (except for temporal sparsity in the surround-sound channels).

In this section, we present two tap selection schemes which apply the M-Max criterion only across the single dimension of filter length, thereby exploiting the temporal sparsity present in the multichannel reference spectra.

Fig. 6 Gini indices for joint spectro-spatio-temporal sparsity for different reference signals.

Figure 6 shows the Gini indices for the joint spectro-spatio-temporal sparsity for the different considered reference signals.

The Gini index for the joint spectro-temporal sparsity in each channel is computed by processing the magnitude spectrogram of that channel and is plotted in Fig. 5 d, along with the joint spectro-spatio-temporal sparsity for all K·R·T coefficients.

Similarly, in Fig. 5 b, the black curve displays the Gini index for the joint spatio-temporal sparsity, computed in every subband on a vector with R·T spectral coefficients.

As this scheme applies the M-Max criterion on the complete vector (underline {mathbf {X}} ell)), it is able to exploit the spectro-spatio-temporal sparsity that may be present in the multichannel reference spectra, with the M selected taps distributed amongst the different sub-filters in every frame.