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Experimental results validate that the proposed adaptive algorithm provide good imperceptibility with good robustness against signal processing attacks at adjustable payload for different types of audio signals.
We can see that different types of audio files have different optimal prediction orders.
Additionally, different types of audio signals show different extension performance in terms of LSD.
With the proposed predictor, the prediction errors can be efficiently reduced for different types of audio files.
Different types of audio signals have different perceptual properties, and therefore, watermarking performance may vary from type to another.
The main reason is that the proposed prediction model can better explore the correlation property of the samples for different types of audio files.
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Fig. 5 Performance of various divisions of our dataset using AdaBoost.M2, KNN, GMM, BPNN, and NBC model Table 3 Different types of combinations of audio features Audio features TL PL TPL MLP MT MLPT MLPTPL Timbre (T) Yes No Yes No Yes Yes Yes Loudness (L) Yes Yes Yes No No No Yes Pitches (P) No Yes Yes No No No Yes MFCC (M) No No No Yes Yes Yes Yes LPC (LP) No No No Yes No Yes Yes.
This study proposes to collect a large amount of different types of data (audio and visual data), from a number of sources (eg, child and adolescent survivors, their family, teachers and health professionals), and at different time points (1, 3 and 6 months from consenting).
Know the different types of MP4 audio files.
The proposed approach is also tested with different types of combinations of audio feature set as shown in Table 3.
Quit Genius uses CBT to take smokers through stages of quitting, using a number of different types of content, from audio sessions to animated videos to interactive exercises to help people think differently about destructive addictions.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com