This significance is computed by comparing twenty five values of mutual information (shift parameter K = 0 to 24) in a paired t-test.
In this paper, the traditional MFCC [10] and Gammatone frequency cepstral coefficients (GFCC) [30] are adopted as the reference WB audio feature vectors to analyze the mutual information between the HF and LF parameters processed by different parameterizations, in comparison to the proposed TSCC.
The packages of c3net and igraph, and self-written procedures, were used for network reconstruction, including estimations of mutual information, adjacency matrix, network parameters and network topological properties, and graphical representations.
We applied Greedy SyN registration with mutual information similarity measurements and set parameters according to the suggestions by Avants et al. (2011).
By using Mel-scale filters and cepstrum analysis, MFCC provides more certainty about the HF components, which is quantified as the ratio of mutual information between the HF and LF parameters to the discrete entropy of HF parameters.
Figures 3a and 3b report the mutual information plotted against the mixing parameter for the synthetic networks to illustrate how close these methods were in revealing the known community structure.
Section 3 gives the analysis of mutual information between the HF and LF parameters processed by different parameterizations.
The proposed method suppresses the transient components existing in the WB audio signal in the BWE frontend and provides higher mutual information between the LF and HF parameters.
The information entropy of DWI-MRI and CT fusion is larger than that of T1-MRI; however, parameters of the mutual information, mean grads, and spatial frequency are all smaller than those of T1-MRI fusion results (Table 3).
