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The details of test datasets are summarized in Table 1.

The results from vocal extraction on the test datasets are presented.

The development and test datasets are used to estimate the parameters and evaluate the performance, respectively.

The F-score performance on the training and test datasets are fully shown in Figure4c,d, respectively.

Details of the training and test datasets are given in Tables 1 and 2. Table 1 Amount of data for the Dev. and Eval.

Experiments of test datasets are reported in "Results and discussion" section while the conclusions and future developments are discussed in "Conclusions" section.

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Our test datasets were acquired using a FARO Focus 3D scanner.

First of all, artificial test datasets were created.

Test datasets were assembled that can be used to compare the numerical results for different software tools.

Then a hybrid test strategy, which can handle missing values in test datasets, is designed for combining the sequential test with the batch test strategy.

Thus, eight test datasets were constructed and each dataset was again divided randomly into four small groups that contained 32, 16, 8, and 4 compounds respectively.

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