Common use of Selection algorithm Clause in Contracts

Selection algorithm. Fig. 2.6 is a flowchart showing the selection algorithm. Its data input are the results obtained for a given criterion (SNRglo, SNRseg, MSE and IS), for 200 × 41 signals (200 speech-noise pairs for which the signals are added according to 41 SNR) denoised using the techniques considered. Here are the different steps performed for each speech-noise pair: a. Only the selected database values that correspond to one of the selection criteria (IS for the second step of the general methodology, SNRglo, SNRseg or MSE for its third step as shown in Fig. 2.2), are considered. b. The set of values to process are divided into 200 sub-sets corresponding to the results ob- tained for each of the 200 speech-noise pairs. c. A minimum-quality test is then performed. Each of the 200 sub-sets, i.e. each speech-noise pair, is tested to ensure that a minimum quality of denoising is obtained. For example, if the sub-set of values considered contains the gains in terms of SNRglo for a given speech- noise pair, this sub-set is first divided into 41 groups according to the 41 possible values of the noised signal SNR. For each of these 41 groups, the maximum gain in terms of SNR is determined. The average of these 41 maximum gains is then calculated. This average of maximum gains must be greater or equal to the preset base value (1 dB for example) to ensure that the speech-noise pair is valid. This base value is chosen depending on the type of criterion, the noised speech signals, the denoising techniques considered and the minimum quality one subsequently wishes to ensure. The study on the speech-noise pair is continued only if the minimum-quality test condition is satisfied. If not, the speech-noise pair considered cannot properly be denoised using the methods considered. d. Each of the M sub-sets that meet the quality-test is divided into 41 groups according to the 41 possible values of the SNR of the noised signal. e. For each of the M × 41 groups, the denoising techniques that provide the best performance results are selected according to the following rule: the average of the performance should not be too far from the extremums obtained for the methods retained. Two parameters, cho- sen according to the type of criterion, the noised speech signals and the denoising techniques considered, enable us to quantify these deviations: an absolute deviation and a relative de- viation between the maximum and minimum values. For example, if the group of values considered contains gains in terms of SNRglo, the absolute deviation between the maximum and minimum values obtained for the retained denoising techniques must be lower than or equal to the preset maximum absolute deviation value (4 dB for example). The same procedure is applied for a preset maximum relative deviation (20% for example). f. For every one of the M speech-noise pairs, the results obtained from the previous step for each of the 41 SNR are examined to identify and retain the techniques that yield the best performance results for the whole set of the 41 SNR values of the noised signal. g. For each of the denoising techniques considered for the algorithm, the percentage of speech- noise pairs, for which this technique yields the best performance for all the 41 SNR of the noised signal, is calculated. This value is the “efficiency” defined is section 2.4.2. The selection algorithm therefore enables us to determine the efficiency of each of the denois- ing techniques considered. The algorithm is blind: it has no information about the techniques it is testing and thus it cannot favour one over another. Set of database values considered Fail Minimum quality test on each subset Pass Efficiency of each technique: the percentage of speech-noise pairs for which this technique gives the best performances