Revisiting quantization theorem through audiowatermarking

Halalchi, Houssem; Mahé, Gaël

doi:10.1109/icassp.2009.4960345

Cited by 4 publications

(14 citation statements)

References 6 publications

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“…the amount of zeros is increased [6], [7], [9] or the shape parameters of their Generalized Gaussian distributions are reduced [8] -in order to enhance source separation [7], [8] or audio-coding [6], [9]. In [10], the histogram is low-pass filtered in order to fulfill the conditions of the quantization theorem [11] and restore the histogram of the signal from that of the sub-quantized signal. In all these applications, the transformation must be perceptually transparent.…”

Section: Introductionmentioning

confidence: 99%

“…While the algorithms operating on time-frequency distributions [6]- [9] control the audibility of the transformation through perceptual models, none of the algorithms operating on time-domain samples [5], [10] provides a satisfactory perceptual control of the transformation.…”

Section: Introductionmentioning

confidence: 99%

“…The proposed algorithm is intended for any application where the original signals to be processed are available and should have a specific distribution for further optimal processing. This distribution is for example Gaussian for non-linear system identification [5], sparse for source separation [12], band-limited for application of the quantization theorem [10], adapted to the error minimization in the optimal quantization [13].…”

Section: Introductionmentioning

confidence: 99%

“…Two levels of reshaping are targeted: changing the global shape of the distribution, as in [5]- [9], or reshaping locally the histogram, as in [10], assuming that the global shape is satisfactory.…”

Section: Introductionmentioning

confidence: 99%

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Perceptually Controlled Reshaping of Sound Histograms

Mahé

2018

IEEE/ACM Trans. Audio Speech Lang. Process.

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Many audio processing algorithms have optimal performance for specific signal statistical distributions that may not be fulfilled for all signals. When the original signal is available, we propose to add an inaudible noise so that the distribution of the signal-plus-noise mixture is as close as possible to a given target distribution. The proposed generic algorithm (independent from the application) adds iteratively a low-power white noise to a flat-spectrum version of the signal, until the target distribution or the noise audibility is reached. The latter is assessed through a frequency masking model. Two implementations of this sound reshaping are described, according to the level of the targeted transformation and to the foreseen application: Histogram Global Reshaping (HGR) to change the global shape of the histogram and Histogram Local Reshaping (HLR) to locally "chisel" the histogram, but keeping the global shape unchanged. These two variants are illustrated by two applications where the inaudibility of the noise generated by the algorithm is required: "sparsification" for source separation, and low-pass filtering of the histogram for application of the quantization theorem, respectively. In both cases, the target histogram is reached or almost reached and the transformation is inaudible. The experiments show that the source separation performs better with HGR and that the HLR allows a better application of the quantization theorem.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Perceptually Controlled Reshaping of Sound Histograms

Mahé

2018

IEEE/ACM Trans. Audio Speech Lang. Process.

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“…The principle is to imperceptibly change the properties of an audio signal in order to improve a particular processing task. For example, in [23], this idea was employed to 'stationarize' audio signals, aiming to enhance acoustic echo cancelation; in [24], the authors proposed a 'gaussianization' procedure for non-linear system identification and [25] proposed a method for reducing the spectral support of the probability density function (PDF) of an audio signal in order to match the conditions of the quantization theorem.…”

Section: Introductionmentioning

confidence: 99%

Perceptually controlled doping for audio source separation

Mahé

Nadalin

Suyama

et al. 2014

EURASIP J. Adv. Signal Process.

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The separation of an underdetermined audio mixture can be performed through sparse component analysis (SCA) that relies however on the strong hypothesis that source signals are sparse in some domain. To overcome this difficulty in the case where the original sources are available before the mixing process, the informed source separation (ISS) embeds in the mixture a watermark, which information can help a further separation. Though powerful, this technique is generally specific to a particular mixing setup and may be compromised by an additional bitrate compression stage. Thus, instead of watermarking, we propose a 'doping' method that makes the time-frequency representation of each source more sparse, while preserving its audio quality. This method is based on an iterative decrease of the distance between the distribution of the signal and a target sparse distribution, under a perceptual constraint. We aim to show that the proposed approach is robust to audio coding and that the use of the sparsified signals improves the source separation, in comparison with the original sources. In this work, the analysis is made only in instantaneous mixtures and focused on voice sources.

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Spread spectrum data embedding in audio with UISA based cooperative detection

Djaziri-Larbi

Turki

2019

Multimed Tools Appl

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Revisiting quantization theorem through audiowatermarking

Cited by 4 publications

References 6 publications

Perceptually Controlled Reshaping of Sound Histograms

Perceptually Controlled Reshaping of Sound Histograms

Perceptually controlled doping for audio source separation

Spread spectrum data embedding in audio with UISA based cooperative detection

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