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A differential acoustic OFDM technique is presented to embed data imperceptibly in existing music. The method allows playing back music containing the data with a speaker without users noticing the embedded data channel. Using a microphone, the data can be recovered from the recording. Experiments with smartphone microphones show that transmission distances of 24 meters are possible, while achieving bit error ratios of less than 10 percent, depending on the environment.

This paper is concerned with estimating unknown multidimensional frequencies from linear compressive measurements. This is accomplished by employing the recently proposed atomic norm minimization framework to recover these frequencies under a sparsity prior without imposing any grid restriction on these frequencies. To this end, we give a rigorous derivation of an iterative scheme called alternating direction of multipliers method, which is able to incorporate multiple compressive snapshots from a multi-dimensional superposition of complex harmonics.

In this paper, we study the denoising of piecewise smooth graph sig-nals that exhibit inhomogeneous levels of smoothness over a graph. We extend the graph trend filtering framework to a family of non-convex regularizers that exhibit superior recovery performance overexisting convex ones. We present theoretical results in the form ofasymptotic error rates for both generic and specialized graph models. We further present an ADMM-based algorithm to solve the proposedoptimization problem and analyze its convergence.

Electric Network Frequency (ENF) analysis is a promising forensic technique for authenticating digital recordings and detecting tampering within the recordings. The validity of ENF analysis heavily relies on high-quality ENF signals extracted from multimedia recordings. In this paper, we propose an ENF signal extraction method for rolling shutter acquired videos using periodic zero-padding. Our analysis shows that the extracted ENF signals using the proposed method are not distorted and the component with the highest signal-to-noise ratio is located at the intrinsic frequency.