Content-Modification System Using Reference Loudness Detection
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Solution Overview
Problem
Content-modification systems face challenges in determining whether a low media-device loudness is due to user volume adjustment or inherent quiet content, leading to missed opportunities for content-replacement operations.
Innovation Solution
A content-presentation device determines the media-device loudness and, if it's below a threshold, obtains a reference loudness to decide whether to perform a content-modification operation, ensuring targeted advertisements are displayed only when the content is audible to the user.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If content-modification operations are carried out whenever media-device loudness is below a threshold, then targeted advertisements can be displayed more frequently, but false positives occur when low loudness is due to inherently quiet content rather than user volume adjustment
Solution Approach 1:
The system performs preliminary actions by obtaining reference loudness data for upcoming content portions before making the decision to carry out content-modification operations. This advance preparation allows the system to compare actual loudness measurements against expected reference values, enabling more accurate determination of whether low loudness is due to user volume adjustment or inherently quiet content.
Solution Approach 2:
The system implements feedback by continuously monitoring media-device loudness and comparing it against reference loudness values for the corresponding content portions. This feedback mechanism allows the system to adapt its content-modification decisions based on the difference between actual and expected loudness levels, improving the reliability of operation selection while maintaining high productivity.
2Measurement precision
If reference loudness is obtained for every content portion, then accuracy of operation selection improves, but system complexity and processing overhead increase
Solution Approach 1:
The system applies local quality by obtaining reference loudness data selectively for specific content portions rather than uniformly for all content. This approach focuses computational resources on obtaining reference data where it is most needed for accurate content-modification decisions, thereby improving measurement precision while managing system complexity through targeted rather than comprehensive data collection.
Data Source
AI summary
In one aspect, an example method includes (i) determining, by a content-presentation device, a media-device loudness of content that is provided to the content-presentation device by a media device, with the content portion being provided to the content-presentation device prior to an upcoming content-modification opportunity; (ii) determining, by the content-presentation device, that the media-device loudness is less than a threshold; (iii) based on determining that the media-device loudness is less than the threshold, obtaining, by the content-presentation device, a reference loudness of the content portion; and (iv) using, by the content-presentation device, the reference loudness as a basis for determining whether or not to carry out a content-modification operation in connection with the upcoming content-modification opportunity.