Dataset Information

Self-recovery scheme for audio restoration using auditory masking.

ABSTRACT: Self-recovery schemes identify and restore tampering, using as a reference a compressed representation of a signal embedded into itself. In addition, audio self-recovery must comply with a transparency threshold, adequate for applications such as on-line music distribution or speech transmission. In this manuscript, an audio self-recovery scheme is proposed. Auditory masking properties of the signals are used to determine the frequencies that better mask the embedding distortion. Frequencies in the Fourier domain are mapped to the intDCT domain for embedding and extraction of reference bits for signal restoration. The contribution of this work is the use of auditory masking properties for the frequency selection and the mapping to the intDCT domain. Experimental results demonstrate that the proposed scheme satisfies a threshold of -2 ODG, suitable for audio applications. The efficacy of the scheme, in terms of its restoration capabilities, is also shown.

SUBMITTER: Menendez-Ortiz A

PROVIDER: S-EPMC6161912 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature

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Self-recovery scheme for audio restoration using auditory masking.

Menendez-Ortiz Alejandra A Feregrino-Uribe Claudia C Garcia-Hernandez Jose Juan JJ

PloS one 20180928 9

Self-recovery schemes identify and restore tampering, using as a reference a compressed representation of a signal embedded into itself. In addition, audio self-recovery must comply with a transparency threshold, adequate for applications such as on-line music distribution or speech transmission. In this manuscript, an audio self-recovery scheme is proposed. Auditory masking properties of the signals are used to determine the frequencies that better mask the embedding distortion. Frequencies in ...[more]

PMID: 30265725

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Project description:Many audio applications perform perception-based time-frequency (TF) analysis by decomposing sounds into a set of functions with good TF localization (i.e. with a small essential support in the TF domain) using TF transforms and applying psychoacoustic models of auditory masking to the transform coefficients. To accurately predict masking interactions between coefficients, the TF properties of the model should match those of the transform. This involves having masking data for stimuli with good TF localization. However, little is known about TF masking for mathematically well-localized signals. Most existing masking studies used stimuli that are broad in time and/or frequency and few studies involved TF conditions. Consequently, the present study had two goals. The first was to collect TF masking data for well-localized stimuli in humans. Masker and target were 10-ms Gaussian-shaped sinusoids with a bandwidth of approximately one critical band. The overall pattern of results is qualitatively similar to existing data for long maskers. To facilitate implementation in audio processing algorithms, a dataset provides the measured TF masking function. The second goal was to assess the potential effect of auditory efferents on TF masking using a modeling approach. The temporal window model of masking was used to predict present and existing data in two configurations: (1) with standard model parameters (i.e. without efferents), (2) with cochlear gain reduction to simulate the activation of efferents. The ability of the model to predict the present data was quite good with the standard configuration but highly degraded with gain reduction. Conversely, the ability of the model to predict existing data for long maskers was better with than without gain reduction. Overall, the model predictions suggest that TF masking can be affected by efferent (or other) effects that reduce cochlear gain. Such effects were avoided in the experiment of this study by using maximally-compact stimuli.

Dataset Information

Self-recovery scheme for audio restoration using auditory masking.

Publications

Self-recovery scheme for audio restoration using auditory masking.

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