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This work aims at uncovering new approaches for audio quality-based applications.
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Mr. Silver suggests a similar approach for audio equipment.
In this paper, a semi-blind multiplicative watermarking approach for audio and speech signals has been presented.
In this paper, an efficient advertisement discovery approach for audio podcasting is proposed to automatically discover and locate repeated advertisement for huge audio podcasting collections.
The high-classification accuracies achieved by the proposed technique clearly demonstrate the potential of a true nonstationary tool in the form of a joint TF approach for audio classification.
This paper presents a novel approach for blind audio watermarking.
While linear prediction (LP) has become immensely popular in speech modeling, it does not seem to provide a good approach for modeling audio signals.
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Experiments performed with a diverse database and the high-classification accuracies achieved by the proposed TFM decomposition feature extraction technique clearly demonstrated the potential of the technique as a true nonstationary tool in the form of a TFM decomposition approach for environmental audio classification.
Ruvolo et al. proposed a novel hierarchical feature selection approach for the audio classification by converting the raw data to three-layer feature representation with a tree structure [ 15].
There are various approaches of audio fingerprinting.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com