Spread spectrum data embedding in audio with UISA based cooperative detection

Abstract

In the context of data embedding in audio for communications, Spread Spectrum (SS)-based techniques, combined with auditory models, are efficient in terms of robustness and perceptual quality of the modified host audio. However, their main drawback is the limited embedding capacity due to the strong interference caused by the host audio. In this work, we combine under-determined Blind Source Separation with a generic SS receiver -made of a Wiener equalizer and a correlation-based demodulator- to efficiently reduce the effect of the host interference. Two blocks are added to the generic receiver: an Under-determined Independent Subspace Analysis (UISA) block is placed after the Wiener equalizer in order to separate the components of the equalized output, and a cooperative detection block is applied to the UISA outputs in order to extract the relevant information from all available components. The UISA block uses Empirical Mode Decomposition to obtain multiple observations of the modified host signal. The performance of the proposed system in terms of bit error rate vs. bit-rate is significantly improved: average error rate is null for bit-rates below 500 bps and smaller than 0.1% for bit-rates reaching 1 kbps. The objective evaluation of the perceptual quality of the modified audio confirms the imperceptibility of the embedded data. The proposed system also exhibits robustness against common signal processing operations such as gain modification, noise, MPEG compression and re-quantization.