Digimarc Corporation

Aparna R Gurijala

Ravi K Sharma

Brett Alan Bradley

Shankar Thagadur Shivappa

Ajith Mulki Kamath

Hugh L Brunk

Vojtech Holub

Yang Bai

Osama M Alattar

Robert G Lyons

Scott M Long

Alastair M Reed

David A Cushman

A method for generating a psychoacoustic model from an audio signal transforms a block of samples of an audio signal into a frequency spectrum comprising frequency components. From this frequency spectrum, it derives group masking energies. These group masking energies each correspond to a group of neighboring frequency components in the frequency spectrum. For a group of frequency components, the method allocates the group masking energy to the frequency components in the group in proportion to energy of the frequency components within the group to provide adapted mask energies for the frequency components within the group, the adapted mask energies providing masking thresholds for the psychoacoustic model of the audio signal.

Human auditory system modeling with masking energy adaptation

Methods and arrangements involving electronic devices, such as smartphones, tablet computers, wearable devices, etc., are disclosed. One arrangement involves a low-power processing technique for discerning cues from audio input. Another involves a technique for detecting audio activity based on the Kullback-Liebler divergence (KLD) (or a modified version thereof) of the audio input. Still other arrangements concern techniques for managing the manner in which policies are embodied on an electronic device. Others relate to distributed computing techniques. A great variety of other features are also detailed.

Methods and system for cue detection from audio input, low-power data processing and related arrangements

Audio signal processing enhances audio watermark embedding and detecting processes. Audio signal processes include audio classification and adapting watermark embedding and detecting based on classification. Advances in audio watermark design include adaptive watermark signal structure data protocols, perceptual models, and insertion methods. Perceptual and robustness evaluation is integrated into audio watermark embedding to optimize audio quality relative the original signal, and to optimize robustness or data capacity. These methods are applied to audio segments in audio embedder and detector configurations to support real time operation. Feature extraction and matching are also used to adapt audio watermark embedding and detecting.

Multi-mode audio recognition and auxiliary data encoding and decoding

Pre-processing modules are configured to compensate for time and pitch scaling and shifting and provide compensated audio frames to a watermark detector. Audio frames are adjusted for time stretching and shrinking and for pitch shifting. Detection metrics are evaluated to identify candidates to a watermark detector. Various schemes are also detailed for tracking modifications made to audio stems mixed into audio tracks, and for accessing a history of modifications for facilitating identification of audio stems and audio tracks comprised of stems. Various approaches address interference from audio overlays added to channels of audio after embedding. One approach applies informed embedding based on phase differences between corresponding components of the channels. A detector extracts the watermark payload effectively from either additive or subtractive combination of the channels because the informed embedding ensures that the watermark survives both types of processing. Other approaches applies different polarity patterns, watermark mappings, or protocol keys to the channels. These techniques enable the watermark to survive ambient mixing, conversion to mono, as well as channel differencing to reduce interference from voice-overs and other audio overlays.

Digital watermarks adapted to compensate for time scaling, pitch shifting and mixing

A dynamic path estimation method reconstructs a program timeline in real time from an incoming stream of audio or visual content in which watermark payloads are redundantly encoded. A receiving device buffers a portion of the incoming signal, executes watermark detection on the contents of the buffer, presents detection results, and then advances the incoming signal in the buffer. Each detection result corresponds to different possible detection paths, as the detector does not reveal the precise position of the watermark payload. The dynamic path estimation method operates on the detection results to determine a global cost function for each possible detection path. As the incoming audio advances through a detection buffer, the method updates cost values for the possible paths, determines a global cost for the paths, and outputs a timeline based on the path of the lowest global cost.

Timeline reconstruction using dynamic path estimation from detections in audio-video signals

The present disclosure relates generally to cell phones and cameras, and to digital watermarking involving such cell phones and cameras. One claim recites a method comprising: measuring distortion introduced by a cell phone camera; using a programmed electronic processor, quantifying the distortion; and providing quantified distortion as feedback to adjust a digital watermark embedding process in view of the distortion introduced by the cell phone camera. The act of quantifying distortion may include, e.g., quantifying a spatial frequency response (SFR) of the cell phone camera. Of course, other claims and combinations are provided too.

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Beaverton, OR, United States of America

Aparna R Gurijala