Context-Aware AGC Gain Selection Using Historical Media Data
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Solution Overview
Problem
Traditional automated gain control (AGC) protocols in media monitoring systems require testing the same range of gains each time, leading to inefficiencies and increased processor resources, as they do not effectively utilize historical data to adjust gain levels based on contextual factors such as user habits, time, and media type.
Innovation Solution
The system employs an AGC parameter determiner that uses historical data and contextual information to adjust the starting gain and range of gains for subsequent AGC protocols, reducing the need for repeated testing and conserving resources by starting at a previously determined optimal gain level.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional AGC protocols test the same range of gains each time, then measurement precision is maintained, but loss of time and processor resources increases
Solution Approach 1:
The system performs preliminary actions by storing historical gain level data and contextual information from previous AGC protocols. Before executing a new AGC protocol, the system retrieves relevant historical data matching the current contextual conditions (time of day, day of week, media type) and uses this pre-acquired information to determine an optimized starting gain level, eliminating the need to test the full gain range from scratch each time.
Solution Approach 2:
The system implements feedback by continuously monitoring and storing the results of AGC protocols along with contextual data. This historical feedback is analyzed to identify patterns and optimize future AGC executions. The system uses the accumulated feedback from previous measurements to adjust and refine the starting gain level selection, improving both speed and accuracy over time.
2Reliability
If traditional AGC protocols test the same range of gains each time, then reliability is maintained, but use of processor resources increases
Solution Approach 1:
The system performs preliminary actions by pre-processing and storing gain level results associated with specific contextual conditions. When an AGC protocol needs to be executed, the system has already prepared the relevant historical data, allowing it to quickly determine an appropriate starting gain level without exhaustive testing, thus reducing processor resource consumption while maintaining reliable signature generation.
Solution Approach 2:
The system changes parameters by dynamically adjusting the starting gain level based on historical data and contextual conditions rather than always beginning at a fixed minimum gain. This parameter adaptation allows the system to skip unnecessary gain level tests while maintaining the reliability of signature generation and watermark extraction by selecting gain levels that have been historically validated for similar conditions.
3Productivity
If historical data is used to adjust gain levels, then productivity is improved, but device complexity increases
Solution Approach 1:
The system performs preliminary actions by organizing and storing historical AGC results with their associated contextual data (time, media type, gain levels) in advance. This pre-organization enables efficient retrieval during actual media monitoring operations, significantly improving productivity without requiring complex real-time processing. The data structure is designed to facilitate quick matching of current conditions with historical patterns.
Solution Approach 2:
The system introduces an intermediary component that manages the historical data storage and retrieval processes. This intermediary layer handles the complexity of data organization, matching contextual conditions, and selecting appropriate starting gain levels, thereby isolating the complexity from the core media monitoring functions and improving overall system productivity without burdening the main processing pathways.
Data Source
AI summary
Methods and apparatus to perform an automated gain control protocol with an amplifier based on historical data corresponding to contextual data are disclosed. An example apparatus includes a controller to select an automatic gain control (AGC) parameter for an AGC protocol based on historical data corresponding to contextual data, the contextual data including at least one of a time during which the AGC protocol is performed, a panelist identified by a meter, demographics of an audience identified by the meter, a location of the meter, a station identified by the meter, a media type identified by the meter, or a sound pressure level identified by the meter; and a processor to perform the AGC protocol based on the selected AGC parameter.