Patents by Inventor William Francis WOLCOTT, IV

William Francis WOLCOTT, IV has filed for patents to protect the following inventions. This listing includes patent applications that are pending as well as patents that have already been granted by the United States Patent and Trademark Office (USPTO).

  • Techniques for computing perceived audio quality based on a trained multitask learning model
    Patent number: 11636872
    Abstract: In various embodiments, a quality inference application estimates perceived audio quality. The quality inference application computes a set of feature values for a set of audio features based on an audio clip. The quality inference application then uses a trained multitask learning model to generate predicted labels based on the set of feature values. The predicted labels specify metric values for metrics that are relevant to audio quality. Subsequently, the quality inference application computes an audio quality score for the audio clip based on the predicted labels.
    Type: Grant
    Filed: June 18, 2020
    Date of Patent: April 25, 2023
    Assignee: NETFLIX, INC.
    Inventors: Chih-Wei Wu, Phillip A. Williams, William Francis Wolcott, IV
  • TECHNIQUES FOR COMPUTING PERCEIVED AUDIO QUALITY BASED ON A TRAINED MULTITASK LEARNING MODEL
    Publication number: 20210350820
    Abstract: In various embodiments, a quality inference application estimates perceived audio quality. The quality inference application computes a set of feature values for a set of audio features based on an audio clip. The quality inference application then uses a trained multitask learning model to generate predicted labels based on the set of feature values. The predicted labels specify metric values for metrics that are relevant to audio quality. Subsequently, the quality inference application computes an audio quality score for the audio clip based on the predicted labels.
    Type: Application
    Filed: June 18, 2020
    Publication date: November 11, 2021
    Inventors: Chih-Wei WU, Phillip A. WILLIAMS, William Francis WOLCOTT, IV
  • TECHNIQUES FOR TRAINING A MULTITASK LEARNING MODEL TO ASSESS PERCEIVED AUDIO QUALITY
    Publication number: 20210350819
    Abstract: In various embodiments, a training application trains a multitask learning model to assess perceived audio quality. The training application computes a set of pseudo labels based on a first audio clip and multiple models. The set of pseudo labels specifies metric values for a set of metrics that are relevant to audio quality. The training application also computes a set of feature values for a set of audio features based on the first audio clip. The training application trains a multitask learning model based on the set of feature values and the set of pseudo labels to generate a trained multitask learning model. In operation, the trained multitask learning model maps different sets of feature values for the set of audio features to different sets of predicted labels. Each set of predicted labels specifies estimated metric values for the set of metrics.
    Type: Application
    Filed: June 18, 2020
    Publication date: November 11, 2021
    Inventors: Chih-Wei WU, Phillip A. WILLIAMS, William Francis WOLCOTT, IV