Techniques for computing perceived audio quality based on a trained multitask learning model
AssigneeNetflix, Inc.
InventorsChih-Wei Wu, Phillip A. Williams, William Francis Wolcott, IV
Life of this patent
Estimating the term…
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.
Claims18
A computer-implemented method for estimating perceived audio quality, the method comprising:
- computing a first set of feature values for a set of audio features based on a first audio clip;
- generating a first plurality of predicted labels via a trained multitask learning model based on the first set of feature values, wherein the first plurality of predicted labels specifies metric values for a plurality of metrics that are relevant to audio quality;
- computing a geometric mean of the first plurality of predicted labels;
- and
- scaling the geometric mean to generate a first audio quality score for the first audio clip.
One or more non-transitory computer readable media including instructions that, when executed by one or more processors, cause the one or more processors to estimate perceived audio quality by performing the steps of:
- computing a first set of feature values for a set of audio features based on a first audio clip;
- causing a trained multitask learning model to generate a first plurality of predicted labels based on the first set of feature values, wherein the first plurality of predicted labels specifies metric values for a plurality of metrics that are relevant to audio quality;
- computing a geometric mean of the first plurality of predicted labels;
- and
- scaling the geometric mean to generate a first audio quality score for the first audio clip.
A system comprising:
- one or more memories storing instructions;
- and
- one or more processors coupled to the one or more memories that, when executing the instructions, perform the steps of:
- computing a first set of feature values for a set of audio features based on a first audio clip;
- generating a first plurality of predicted labels via a trained multitask learning model based on the first set of feature values, wherein the first plurality of predicted labels specifies metric values for a plurality of metrics that are relevant to audio quality;
- performing at least one aggregation operation on the first plurality of predicted labels to compute an unscaled audio quality score for the first audio clip;
- and
- scaling the unscaled audio quality score to generate an audio quality score for the first audio clip.
Dependencies are inferred from the claim text.