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Experimental results show that the proposed method outperforms conventional stereo matching algorithms with occlusion handling.
The experiment results in isolated word recognition under clean and reverberant conditions showed that the proposed method outperforms conventional MFCC.
Experimental results show that the proposed method outperforms conventional edge-directed methods on objective and subjective criteria.
Moreover, according to our results, the proposed method outperforms conventional methods, as presented in the next section.
The simulation results indicated that the proposed method outperforms conventional edge directed algorithms with respect to both objective and subjective criteria.
Experimental results on a variety of datasets showed that our method outperforms conventional methods and both steps in our system are essential for the overall performance.
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Simulation results show that the proposed method outperformed conventional methods both visually and numerically.
For separation performance in the BSS_EVAL criteria, our method outperformed conventional complex ISFA under all conditions.
The proposed method outperformed conventional methods, which was confirmed with objective metrics and a comparison using actual images.
The results showed that the suggested method outperformed conventional fusion of the features and classification units using the majority voting method.
Our proposed method outperformed conventional methods for obtaining the CI under regular recording, impulsive, and white Gaussian noisy conditions.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com