Abstract: Systems and methods are disclosed for manually and programmatically remediating websites to thereby facilitate website navigation by people with diverse abilities. For example, an administrator portal is provided for simplified, form-based creation and deployment of remediation code, and a machine learning system is utilized to create and suggest remediations based on past remediation history. Voice command systems and portable document format (PDF) remediation techniques are also provided for improving the accessibility of such websites.

Abstract: Methods and systems for manual and programmatic remediation of websites. JavaScript code is accessed by a user device and optionally calls TTS, ASR, and RADAE modules from a remote server to thereby facilitate website navigation by people with diverse abilities.

Abstract: Embodiments are disclosed for generating full-band audio from narrowband audio using a GAN-based audio super resolution model. A method of generating full-band audio may include receiving narrow-band input audio data, upsampling the narrow-band input audio data to generate upsampled audio data, providing the upsampled audio data to an audio super resolution model, the audio super resolution model trained to perform bandwidth expansion from narrow-band to wide-band, and returning wide-band output audio data corresponding to the narrow-band input audio data.

Abstract: Methods and systems for manual and programmatic remediation of websites. JavaScript code is accessed by a user device and optionally calls TTS, ASR, and RADAE modules from a remote server to thereby facilitate website navigation by people with diverse abilities.

Abstract: Methods and systems for manual and programmatic remediation of websites. JavaScript code is accessed by a user device and optionally calls TTS, ASR, and RADAE modules from a remote server to thereby facilitate website navigation by people with diverse abilities.

Abstract: Embodiments are disclosed for converting audio data to studio quality audio data. The method includes obtaining an audio data having a first quality for conversion to studio quality audio. A first machine learning model predicts a set of acoustic features. A spectral mask is applied to the audio data during the prediction of the set of acoustic features. A second machine learning model generates studio quality audio from the set of acoustic features and the audio data.

Abstract: Discontinuity modeling techniques of computing functions of a program are described. In one example, a program has a computing function that includes a discontinuity. An input is received by the data modeling system that identifies an axis. A plurality of samples is then generated by the data modeling system along the axis based on an output of the program. The samples are then used as a basis by the data modeling system to generate a data model that models the discontinuity. The data model includes, in one example, one or more gradients and models the discontinuity using a 1D box kernel.

Abstract: Systems and methods are disclosed for manually and programmatically remediating websites to thereby facilitate website navigation by people with diverse abilities. For example, an administrator portal is provided for simplified, form-based creation and deployment of remediation code, and a machine learning system is utilized to create and suggest remediations based on past remediation history. Voice command systems and portable document format (PDF) remediation techniques are also provided for improving the accessibility of such websites.

Abstract: Systems and methods are disclosed for manually and programmatically remediating websites to thereby facilitate website navigation by people with diverse abilities. For example, an administrator portal is provided for simplified, form-based creation and deployment of remediation code, and a machine learning system is utilized to create and suggest remediations based on past remediation history. Voice command systems and portable document format (PDF) remediation techniques are also provided for improving the accessibility of such websites.

Abstract: Methods and systems for manual and programmatic remediation of websites. JavaScript code is accessed by a user device and optionally calls TTS, ASR, and RADAE modules from a remote server to thereby facilitate website navigation by people with diverse abilities.

Abstract: Systems and methods are disclosed for manually and programmatically remediating websites to thereby facilitate website navigation by people with diverse abilities. For example, an administrator portal is provided for simplified, form-based creation and deployment of remediation code, and a machine learning system is utilized to create and suggest remediations based on past remediation history. Voice command systems and portable document format (PDF) remediation techniques are also provided for improving the accessibility of such websites.

Abstract: Operations of a method include receiving a request to enhance a new source audio. Responsive to the request, the new source audio is input into a prediction model that was previously trained. Training the prediction model includes providing a generative adversarial network including the prediction model and a discriminator. Training data is obtained including tuples of source audios and target audios, each tuple including a source audio and a corresponding target audio. During training, the prediction model generates predicted audios based on the source audios. Training further includes applying a loss function to the predicted audios and the target audios, where the loss function incorporates a combination of a spectrogram loss and an adversarial loss. The prediction model is updated to optimize that loss function. After training, based on the new source audio, the prediction model generates a new predicted audio as an enhanced version of the new source audio.

Abstract: Systems and methods are disclosed for manually and programmatically remediating websites to thereby facilitate website navigation by people with diverse abilities. For example, an administrator portal is provided for simplified, form-based creation and deployment of remediation code, and a machine learning system is utilized to create and suggest remediations based on past remediation history. Voice command systems and portable document format (PDF) remediation techniques are also provided for improving the accessibility of such websites.

Abstract: Methods and systems for manual and programmatic remediation of websites. JavaScript code is accessed by a user device and optionally calls TTS, ASR, and RADAE modules from a remote server to thereby facilitate website navigation by people with diverse abilities.

Abstract: Systems and methods are disclosed for manually and programmatically remediating websites to thereby facilitate website navigation by people with diverse abilities. For example, an administrator portal is provided for simplified, form-based creation and deployment of remediation code, and a machine learning system is utilized to create and suggest remediations based on past remediation history. Voice command systems and portable document format (PDF) remediation techniques are also provided for improving the accessibility of such websites.

Abstract: This disclosure generally relates to character animation. More specifically, this disclosure relates to pose selection using data analytics techniques applied to training data, and generating 2D animations of illustrated characters using performance data and the selected poses. An example process or system includes extracting sets of joint positions from a training video including the subject, grouping the plurality of frames into frame groups using the sets of joint positions for each frame, identifying a representative frame for each frame group using the frame groups, clustering the frame groups into clusters using the representative frames, outputting a visualization of the clusters at a user interface, and receiving a selection of a cluster for animation of the subject.

Abstract: This disclosure generally relates to character animation. More specifically, but not by way of limitation, this disclosure relates to pose selection using data analytics techniques applied to training data, and generating 2D animations of illustrated characters using performance data and the selected poses. An example process or system includes obtaining a selection of training poses of the subject and a set of character poses, obtaining a performance video of the subject, wherein the performance video includes a plurality of performance frames that include poses performed by the subject, grouping the plurality of performance frames into groups of performance frames, assigning a selected training pose from the selection of training poses to each group of performance frames using the clusters of training frames, generating a sequence of character poses based on the groups of performance frames and their assigned training poses, outputting the sequence of character poses.

Abstract: Operations of a method include receiving a request to enhance a new source audio. Responsive to the request, the new source audio is input into a prediction model that was previously trained. Training the prediction model includes providing a generative adversarial network including the prediction model and a discriminator. Training data is obtained including tuples of source audios and target audios, each tuple including a source audio and a corresponding target audio. During training, the prediction model generates predicted audios based on the source audios. Training further includes applying a loss function to the predicted audios and the target audios, where the loss function incorporates a combination of a spectrogram loss and an adversarial loss. The prediction model is updated to optimize that loss function. After training, based on the new source audio, the prediction model generates a new predicted audio as an enhanced version of the new source audio.

Abstract: Methods and systems for manual and programmatic remediation of websites. JavaScript code is accessed by a user device and optionally calls TTS, ASR, and RADAE modules from a remote server to thereby facilitate website navigation by people with diverse abilities.

Abstract: Systems and methods are disclosed for manually and programmatically remediating websites to thereby facilitate website navigation by people with diverse abilities. For example, an administrator portal is provided for simplified, form-based creation and deployment of remediation code, and a machine learning system is utilized to create and suggest remediations based on past remediation history. Voice command systems and portable document format (PDF) remediation techniques are also provided for improving the accessibility of such websites.