Abstract: A system and method for displaying images of internal anatomy includes an image processing device configured to provide high resolution images of the surgical field from low resolution scans during the procedure. The image processing device digitally manipulates a previously-obtained high resolution baseline image to produce many representative images based on permutations of movement of the baseline image. During the procedure a representative image is selected having an acceptable degree of correlation to the new low resolution image. The selected representative image and the new image are merged to provide a higher resolution image of the surgical field. The image processing device is also configured to provide interactive movement of the displayed image based on movement of the imaging device, and to permit placement of annotations on the displayed image to facilitate communication between the radiology technician and the surgeon.

Abstract: Techniques for identifying a trusted SSID for a wireless network are disclosed. Prior to establishing a connection with a wireless network, a first network message is received from a first access point (AP) identifying a first service set identifier (SSID) associated with a first wireless network, a second network message is received from a second AP identifying a second SSID associated with a second wireless network, and a visual similarity is determined between a first visual representation of the first SSID and a second visual representation of the second SSID. The second SSID is designated as suspicious based on the determined visual similarity.

Abstract: The derivation of personalized HRTFs for a human subject based on the anthropometric feature parameters of the human subject involves obtaining multiple anthropometric feature parameters and multiple HRTFs of multiple training subjects. Subsequently, multiple anthropometric feature parameters of a human subject are acquired. A representation of the statistical relationship between the plurality of anthropometric feature parameters of the human subject and a subset of the multiple anthropometric feature parameters belonging to the plurality of training subjects is determined. The representation of the statistical relationship is then applied to the multiple HRTFs of the plurality of training subjects to obtain a set of personalized HRTFs for the human subject.

Abstract: The derivation of personalized HRTFs for a human subject based on the anthropometric feature parameters of the human subject involves obtaining multiple anthropometric feature parameters and multiple HRTFs of multiple training subjects. Subsequently, multiple anthropometric feature parameters of a human subject are acquired. A representation of the statistical relationship between the plurality of anthropometric feature parameters of the human subject and a subset of the multiple anthropometric feature parameters belonging to the plurality of training subjects is determined. The representation of the statistical relationship is then applied to the multiple HRTFs of the plurality of training subjects to obtain a set of personalized HRTFs for the human subject.

Abstract: The derivation of personalized HRTFs for a human subject based on the anthropometric feature parameters of the human subject involves obtaining multiple anthropometric feature parameters and multiple HRTFs of multiple training subjects. Subsequently, multiple anthropometric feature parameters of a human subject are acquired. A representation of the statistical relationship between the plurality of anthropometric feature parameters of the human subject and a subset of the multiple anthropometric feature parameters belonging to the plurality of training subjects is determined. The representation of the statistical relationship is then applied to the multiple HRTFs of the plurality of training subjects to obtain a set of personalized HRTFs for the human subject.

Abstract: The derivation of personalized HRTFs for a human subject based on the anthropometric feature parameters of the human subject involves obtaining multiple anthropometric feature parameters and multiple HRTFs of multiple training subjects. Subsequently, multiple anthropometric feature parameters of a human subject are acquired. A representation of the statistical relationship between the plurality of anthropometric feature parameters of the human subject and a subset of the multiple anthropometric feature parameters belonging to the plurality of training subjects is determined. The representation of the statistical relationship is then applied to the multiple HRTFs of the plurality of training subjects to obtain a set of personalized HRTFs for the human subject.

Abstract: The derivation of personalized HRTFs for a human subject based on the anthropometric feature parameters of the human subject involves obtaining multiple anthropometric feature parameters and multiple HRTFs of multiple training subjects. Subsequently, multiple anthropometric feature parameters of a human subject are acquired. A representation of the statistical relationship between the plurality of anthropometric feature parameters of the human subject and a subset of the multiple anthropometric feature parameters belonging to the plurality of training subjects is determined. The representation of the statistical relationship is then applied to the multiple HRTFs of the plurality of training subjects to obtain a set of personalized HRTFs for the human subject.

Abstract: The derivation of personalized HRTFs for a human subject based on the anthropometric feature parameters of the human subject involves obtaining multiple anthropometric feature parameters and multiple HRTFs of multiple training subjects. Subsequently, multiple anthropometric feature parameters of a human subject are acquired. A representation of the statistical relationship between the plurality of anthropometric feature parameters of the human subject and a subset of the multiple anthropometric feature parameters belonging to the plurality of training subjects is determined. The representation of the statistical relationship is then applied to the multiple HRTFs of the plurality of training subjects to obtain a set of personalized HRTFs for the human subject.

Abstract: The derivation of personalized HRTFs for a human subject based on the anthropometric feature parameters of the human subject involves obtaining multiple anthropometric feature parameters and multiple HRTFs of multiple training subjects. Subsequently, multiple anthropometric feature parameters of a human subject are acquired. A representation of the statistical relationship between the plurality of anthropometric feature parameters of the human subject and a subset of the multiple anthropometric feature parameters belonging to the plurality of training subjects is determined. The representation of the statistical relationship is then applied to the multiple HRTFs of the plurality of training subjects to obtain a set of personalized HRTFs for the human subject.

Abstract: Systems, methods, and computer program products are provided for editing digital audio data. In some implementations a method is provided that includes receiving digital audio data, identifying a modification to a portion of the digital audio data, and automatically correcting audio data surrounding one or more edit boundaries resulting from the identified modification including interpolating audio data from a region associated with the one or more edit boundaries.

Abstract: This specification describes technologies relating to editing digital audio data. In some implementations, a computer-implemented method is provided. The method includes displaying a visual representation of audio data, receiving an image, positioning the image as overlapping a portion of the displayed visual representation of the audio data, and editing the audio data corresponding to the portion of the displayed visual representation of the audio data overlapped by the image to form edited audio data, the editing including applying a variable editing effect according to the image content. In other implementations a computer-implemented method is provided that includes receiving an image, converting the image into a display of a visual representation of audio data using a specified conversion mode, and storing audio data corresponding to the display of audio data.

Abstract: The derivation of personalized HRTFs for a human subject based on the anthropometric feature parameters of the human subject involves obtaining multiple anthropometric feature parameters and multiple HRTFs of multiple training subjects. Subsequently, multiple anthropometric feature parameters of a human subject are acquired. A representation of the statistical relationship between the plurality of anthropometric feature parameters of the human subject and a subset of the multiple anthropometric feature parameters belonging to the plurality of training subjects is determined. The representation of the statistical relationship is then applied to the multiple HRTFs of the plurality of training subjects to obtain a set of personalized HRTFs for the human subject.

Abstract: Systems, methods, and computer program products are provided for editing digital audio data. In some implementations a method is provided that includes receiving digital audio data, identifying a modification to a portion of the digital audio data, and automatically correcting audio data surrounding one or more edit boundaries resulting from the identified modification including interpolating audio data from a region associated with the one or more edit boundaries.

Abstract: This specification describes technologies relating to editing digital audio data. In some implementations, a computer-implemented method is provided. The method includes displaying a visual representation of audio data, receiving an image, positioning the image as overlapping a portion of the displayed visual representation of the audio data, and editing the audio data corresponding to the portion of the displayed visual representation of the audio data overlapped by the image to form edited audio data, the editing including applying a variable editing effect according to the image content. In other implementations a computer-implemented method is provided that includes receiving an image, converting the image into a display of a visual representation of audio data using a specified conversion mode, and storing audio data corresponding to the display of audio data.

Abstract: Systems and methods for audio editing are provided. In one implementation, a computer-implemented method is provided. The method includes receiving digital audio data including a plurality of distinct vocal components. Each distinct vocal component is automatically identified using one or more attributes that uniquely identify each distinct vocal component. The audio data is separated into two or more individual tracks where each individual track comprises audio data corresponding to one distinct vocal component. The separated individual tracks are then made available for further processing.

Abstract: Systems and methods for audio editing are provided. In one implementation, a computer-implemented method is provided. The method includes receiving digital audio data including a plurality of distinct vocal components. Each distinct vocal component is automatically identified using one or more attributes that uniquely identify each distinct vocal component. The audio data is separated into two or more individual tracks where each individual track comprises audio data corresponding to one distinct vocal component. The separated individual tracks are then made available for further processing.

Abstract: Systems and methods for editing digital audio data are provided. In one implementation, a method is provided that includes receiving digital audio data. The computer implemented method includes receiving digital audio data and displaying a representation of the digital audio data. The method also includes receiving a selection including a portion of the digital audio data, analyzing a feature of the audio data for the selected portion with respect to a plurality of frequency bands in the digital audio data, calculating a feature value for each frequency band over a specified period of time, and using the feature value for each frequency band to adjust the feature of the audio data.

Abstract: Systems, methods, and apparatus, including computer program products, for audio editing are provided. In some implementations, a method is provided. The method includes receiving audio data having a first audio channel and a second audio channel. The audio data is separated, into a plurality of blocks. An amount of misalignment is determined between the first audio channel and the second audio channel for the portion of the audio data in each block using a phase difference between the first and second audio channels for each of a plurality of frequency bands. The first and second channels are aligned using the determined misalignment.

Abstract: A system and method for processing multi-channel audio signals is described herein. In one embodiment, the system includes a phase detector to determine, for a frequency band, a phase difference between first and second channel signals of the multi-channel digital audio signal. In one embodiment, the system also includes an attenuator to attenuate an amplitude of the frequency band if the phase difference exceeds a first predetermined threshold.

Abstract: This specification describes technologies relating to editing digital audio data. In some implementations, a computer-implemented method is provided. The method includes displaying a visual representation of audio data, receiving an input selecting a selected portion of audio data within the visual representation, the selecting including applying a brush tool to the visual representation of the audio data, and editing the selected portion of audio data including determining a degree of opacity for the selected audio data and applying an editing effect according to the degree of opacity.