公开(公告)号：US20210350820A1

公开(公告)日：2021-11-11

申请号：US16905810

申请日：2020-06-18

Applicant: NETFLIX, INC.

Inventor： Chih-Wei WU ,  Phillip A. WILLIAMS ,  William Francis WOLCOTT, IV

IPC: G10L25/60 ,  G10L25/27 ,  G06K9/62 ,  G06N20/00 ,  G06F17/18

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.

公开(公告)号：US20210350819A1

公开(公告)日：2021-11-11

申请号：US16905793

申请日：2020-06-18

Applicant: NETFLIX, INC.

Inventor： Chih-Wei WU ,  Phillip A. WILLIAMS ,  William Francis WOLCOTT, IV

IPC: G10L25/60 ,  G10L25/27 ,  G06K9/62 ,  G06N20/00 ,  G06F17/18

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.