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Here, we clarify the methods for audio and visual feature extraction.
BWE methods for audio signals can be separated into two tasks: estimation of the spectral envelope and extension of the fine spectrum.
Furthermore, as a major confirmation of the potential of these technologies in perceptual quality assessment, a CI-based framework has been adopted in multiple methods for audio quality assessment, including the ITU standard, PEAQ [25].
We also tested the proposed methodology using perception evaluation methods for audio source separation (PEASS) toolkit [41], which describes a set of four perceptual scores (PS): overall (OPS), target-related (TPS), interference-related (IPS), and artifacts-related (APS), generated through a nonlinear mapping of the PEMO-Q auditory model [42].
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Sridevi et al. [3] presented a method for audio steganography.
We presented a method for audio texture creation for virtual location-exploration services.
Fig. 1 Block diagram of the proposed blind bandwidth extension method for audio signals.
A bandwidth extension method for audio signals based on ensemble echo state network is proposed in this paper.
The human versus non-human sound discrimination is also performed using GMM as a successful traditional clustering method for audio signals.
Since using MFCC features with a SVM algorithm is a generally accepted classification method for audio, we utilized its results to benchmark the CNN algorithm.
In addition, we can also find that the LSD of the PSR method is lower than EHBE and LE methods for complicated audio signals and singing audio signals.
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