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Abstract

Watermark data is encoded in a digitized signal by forming a noise threshold spectrum which represents a maximum amount of imperceptible noise, spread-spectrum chipping the noise threshold spectrum with a relatively endless stream of pseudo-random bits to form a basis signal, dividing the basis signal into segments, and filtering the segments to smooth segment boundaries. The data encoded in the watermark signal is precoded to make the watermark data inversion robust and is convolutional encoded to further increase the likelihood that the watermark data will subsequently be retrievable notwithstanding lossy processing of the watermarked signal. A watermark alignment module determines which of a large number of offsets of the watermarked data is most likely to correspond to a recognizable watermark. The watermark alignment module uses a single basis signal to evaluate a number of offsets over a relatively narrow range of offsets. In addition, offsets which differ by an integer multiple of a spatial/temporal granularity of respective noise threshold spectra are recognized as corresponding to equivalent noise threshold spectra. Accordingly, a previously generated noise threshold spectrum for one offset is reused for a second offset which differs by an integer multiple of the spatial/temporal granularity.

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