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This paper proposes a high capacity and security audio steganography technique to hide secret audio signal.
On the other hand, the dispersion effect in the stealth channel can be employed to hide the stealth signal in the time domain.
We then propose a scheme to hide synchronization marks through the modulation of the embedded signal by a content-based pseudo-random signal.
The reversible data hiding method can not only hide secret messages but also completely restore the original ECG signal after bit extraction.
Proceedings ArticleDOI
Qiang Cheng, Jeffrey Sorensen 
07 May 2001
86 Citations
However, using speech analysis techniques, one may design an effective data signal that can be used to hide an arbitrary message in a speech signal.
The proposed approach applies efficient software techniques that hide development from the users and enable App creation through a simple Web User Interface.
Further, our algorithm can utilize psychoacoustic methods to hide changes of the original audio signal below the human thresholds of hearing.
Intention to hide was higher than intention to unfriend contacts, implying that unfriending is harsher.

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How do different components of an information management system interact to facilitate efficient information management?
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When was the ucr dataset introduced?
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How do hearing aids work?
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Hearing aids function through various mechanisms. They can amplify ambient sounds by selectively emitting white noise to enhance the ear's sensitivity threshold. Additionally, some hearing aids utilize electromyography to detect muscle activity in the auricular muscle, decoding the user's intention to adjust the device's operating mode. Furthermore, a hearing aid may include a wireless interface, processor, and antenna to emit and receive electromagnetic fields, with a decoupling element ensuring optimal performance at the antenna's frequency. Moreover, advanced hearing aid systems continuously monitor audio input signals and EEG-related brain activity to adjust processing modes based on signal comparisons and cognitive load assessments. Finally, adaptive beamforming techniques with complex frequency-dependent parameters and covariance smoothing are employed to enhance signal processing in hearing aids.
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In knowledge distillation for audio classification, various losses are employed to enhance the learning process. One of the losses utilized is the contrastive loss, which focuses on distilling informative contrastive distribution and fine-grained information from spectrogram representations of signals. Additionally, a feature-invariant contrastive loss is proposed to generate high-quality and feature-rich Mel-spectrograms in data-free knowledge distillation frameworks for sound classification tasks. Moreover, a new loss method based on feature distribution is introduced to optimize the loss functions of different distillation substages during training processes in weakly supervised learning frameworks for video classification, which can also be applicable to audio tasks. These losses collectively contribute to improving the performance of student models in audio classification through knowledge distillation techniques.
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How do two way mirrors work from a technical perceptive?
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