Abstract: 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.

Abstract: An audio playback system receives digitally watermarked audio programming and distributes it to audio speakers in a venue, enabling a variety of location and product dependent services to be delivered to mobile devices in the venue. Mobile devices sense audio from speakers and decode digital identifying information, including characteristics to distinguish audio sources. The mobile device communicates with a networked computer to provide the identifying information, which in turn, triggers an alert for output on the mobile device.

Abstract: The present disclosure relates generally to signal processing techniques for content signals such as audio, images and video signals. More particularly, the present disclosure relates to processing content signals to facilitate recognition of ambient content signals using digital watermarks and/or digital fingerprints.

Abstract: In a particular aspect, a multimedia device includes one or more sensors configured to generate first sensor data and second sensor data. The first sensor data is indicative of a first position at a first time and the second sensor data is indicative of a second position at a second time. The multimedia device further includes a processor coupled to the one or more sensors. The processor is configured to generate a first version of a spatialized audio signal, determine a cumulative value based on an offset, the first position, and the second position, and generate a second version of the spatialized audio signal based on the cumulative value.

Abstract: 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.

Abstract: 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.

Abstract: In a particular aspect, an audio processing device includes a position predictor configured to determine predicted position data based on position data. The audio processing device further includes a processor configured to generate an output spatialized audio signal based on the predicted position data.

Abstract: Reference imagery of dermatological conditions is compiled in a crowd-sourced database (contributed by clinicians and/or the lay public), together with associated diagnosis information. A user later submits a query image to the system (e.g., captured with a smartphone). Image-based derivatives for the query image are determined (e.g., color histograms, FFT-based metrics, etc.), and are compared against similar derivatives computed from the reference imagery. This comparison identifies diseases that are not consistent with the query image, and such information is reported to the user. Depending on the size of the database, and the specificity of the data, 90% or more of candidate conditions may be effectively ruled-out, possibly sparing the user from expensive and painful biopsy procedures, and granting some peace of mind (e.g., knowledge that an emerging pattern of small lesions on a forearm is probably not caused by shingles, bedbugs, malaria or AIDS).

Abstract: An audio playback system receives digitally watermarked audio programming and distributes it to audio speakers in a venue, enabling a variety of location and product dependent services to be delivered to mobile devices in the venue. Mobile devices sense audio from speakers and decode digital identifying information, including characteristics to distinguish audio sources. The mobile device communicates with a networked computer to provide the identifying information, which in turn, triggers an alert for output on the mobile device.

Abstract: 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.

Abstract: 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.

Abstract: Audio sounds are captured from a subject's body, e.g., using a smartphone or a worn array of microphones. Plural features are derived from the captured audio, and serve as fingerprint information. One such feature may be a time interval over which a threshold part of spectral energy in the audio is expressed. Another may be a frequency bandwidth within which a second threshold part of the spectral energy is expressed. Such fingerprint information is provided to a knowledge base that contains reference fingerprint data and associated metadata. The knowledge base matches the fingerprint with reference fingerprint data, and provides associated metadata in return—which can comprise diagnostic information related to the captured sounds. In some arrangements, an audio signal or pressure waveform stimulates the body at one location, and is sensed at another, to discern information about the intervening transmission medium. A great variety of other features and arrangements are also detailed.

Abstract: An audio playback system receives digitally watermarked audio programming and distributes it to audio speakers in a venue, enabling a variety of location and product dependent services to be delivered to mobile devices in the venue. Mobile devices sense audio from speakers and decode digital identifying information, including characteristics to distinguish audio sources. The mobile device communicates with a networked computer to provide the identifying information, which in turn, triggers an alert for output on the mobile device.

Abstract: 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.

Abstract: Audio watermark encoding methods employ reversing polarity and pairwise embedding. A watermark signal is generated, mapped to pairs of embedding locations and inserted in the members of the pair with reverse polarity. The pairs of embedding locations correspond to adjacent regions or frames in time or frequency domains. A method for controlling an audio output device captures audio from the device, extracts its identity from a watermark, and modifies the operation of the device, e.g. varying watermark strength or audio loudness.

Abstract: A positioning network comprises an array of signal sources that transmit signals with unique characteristics that are detectable in signals captured through a sensor on a mobile device, such as a microphone of a mobile phone handset. Through signal processing of the captured signal, the positioning system distinguishes these characteristics to identify distinct sources and their corresponding coordinates. A position calculator takes these coordinates together with other attributes derived from the received signals from distinct sources, such as time of arrival or signal strength, to calculate coordinates of the mobile device. A layered protocol is used to introduce distinguishing characteristics in the source signals. This approach enables the use of low cost components to integrate a positioning network on equipment used for other functions, such as audio playback equipment at shopping malls and other venues where location based services are desired.

Abstract: An audio playback system receives digitally watermarked audio programming and distributes it to audio speakers in a venue, enabling a variety of location and product dependent services to be delivered to mobile devices in the venue. Mobile devices sense audio from speakers and decode digital identifying information, including characteristics to distinguish audio sources. The mobile device communicates with a networked computer to provide the identifying information, which in turn, triggers an alert for output on the mobile device.

Abstract: A method for indoor navigation in a venue derives positioning of a mobile device based on sounds captured by the microphone of the mobile device from the ambient environment. It is particularly suited to operate on smartphones, where the sounds are captured using microphone that captures sounds in a frequency range of human hearing. The method determines a position of the mobile device in the venue based on identification of the audio signal, monitors the position of the mobile device, and generates a position based alert on an output device of the mobile device when the position of the mobile device is within a pre-determined position associated with the position based alert.

Abstract: Mobile device positioning employs various forms of audio signal structures and detection methodologies. In one method, detection of an audio signal from a first source enables construction of a signal to facilitate detection of an audio signal from another source. Signals detected from these sources enable positioning of the mobile device receiving those signals. Another method forms audio signals transmitted from audio sources so that they have parts that add constructively and parts that differentiate the sources to enable positioning. Another audio signal based positioning method adaptively switches among positioning methods so that positioning remains operative as a mobile device moves toward and away from the sources. Another method tracks positioning history, evaluates it for errors and performs error mitigation to improve accuracy. Various other positioning technologies are detailed as well.

Abstract: Audio sounds are captured from a subject's body, e.g., using a smartphone or a worn array of microphones. Plural features are derived from the captured audio, and serve as fingerprint information. One such feature may be a time interval over which a threshold part of spectral energy in the audio is expressed. Another may be a frequency bandwidth within which a second threshold part of the spectral energy is expressed. Such fingerprint information is provided to a knowledge base that contains reference fingerprint data and associated metadata. The knowledge base matches the fingerprint with reference fingerprint data, and provides associated metadata in return—which can comprise diagnostic information related to the captured sounds. In some arrangements, an audio signal or pressure waveform stimulates the body at one location, and is sensed at another, to discern information about the intervening transmission medium. A great variety of other features and arrangements are also detailed.