First step. Selecting the adequate wavelet type and the number of analysis levels a. At this point, the entire database is considered. b. The 42 508 800 values of the database are divided into 4 sets of 10 677 200 values. Each set contains values obtained for each of the four selection criteria (SNRglo, SNRseg, MSE and IS). c. Each of the 4 sets is divided into n sub-sets (8 for the wavelet types and 3 for the number of analysis levels) according to the possible options for the parameter considered. As an example, for wavelet types, a sub-set represents the set of results obtained during the use of Daubechies wavelets of order 1 (db1), another for db4, another for db8, another for Symlet wavelets of order 4 (sym4), etc. d. Each of these 4 × n sub-sets is divided into 41 groups according to 41 possible values of the SNR of the noised signal (see Table 2.1). e. For each of these 4 × n × 41 groups, the average for the 200 (20 × 10) speech-noise pairs for the best performance results (max or min) obtained is calculated. f. Four figures (see for example Figs. 2.7 and 2.8 which will be presented in section 2.5.1), one per selection criterion, show these averages, according to the SNR of the noised signal for each of the n possible options for the parameter considered. g. The option for the considered parameter (wavelet type or number of analysis levels) provid- ing the best average of the best performance results for the entire set of SNR of the noised signal, according to the four selection criteria (SNRglo, SNRseg, MSE and IS), is chosen.
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Sources: Thesis Submission, Thesis Submission