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Next is the simulation results and analysis. The recognition performance of the TFA-CNN detector considering the trained dataset with different overlap ratio is shown in Fig. 4. It is observed that the accuracy of the proposed model achieves the highest when the overlap ratio is about 50%(fifty percent). Meanwhile, the recognition performance becomes the worst with a 40% overlap ratio. This phenomenon happens since the time and frequency domain resolutions after STFT pre-processing cannot be achieved simultaneously. Moreover, the overlap ratio affects the quality of the generated images, influencing the model performance directly. In order to achieve a balance between the time and frequency domain resolutions of the generated images and the model recognition performance, we choose the overlap ratio as 50%. |
IEEE/ICACT20230135 Slide.12
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