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Fig. 13 Average accuracy rates of human-assisted sound labelling.

Fig. 14 Average accuracy rates of sound event recognition with respect to different SNRs.

The average accuracy rates and the standard derivations are listed in Table 6.

The testing results without sound separation are very poor with average accuracy rates below 60%%.

The average accuracy rates of sound event recognition with respect to SNRs are shown in Fig. 14.

The experimental results show that the average accuracy rates of recognition using the WPT, SWT and TFDH features are 91.64%, 94.31%, and 91.56%, respectively.

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Table 4 indicated the average accuracy rate for each video in all lanes.

Table 3 shows the overall obtained average accuracy rate and the dataset used for each classification scenario.

Moreover, the average accuracy rate of our prediction algorithm with one known position is about 86.8%, which is 43.9% better than the Markov-based algorithm.

It was found that the average accuracy rate approached 73%% when the penalty factor was 0.01, the epsilon 0.2, the gamma 0.05, and the range 0.5.

Particularly maximum entropy classifier can still get an average accuracy rate as 97.0% after the introduction of the emergency domain terms.

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