Context-Based AGC Parameter Selection for Faster Amplifier Gain Control
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Solution Overview
Problem
Traditional automated gain control (AGC) protocols in media monitoring systems require testing a range of gain levels each time, leading to inefficiencies and resource wastage, as they do not effectively utilize historical data to adjust gain settings 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 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 a full range of gain levels each time, then accurate gain settings are ensured, but time and processor resources are wasted
Solution Approach 1:
The system performs preliminary actions by storing historical AGC results and contextual data (media type, time, user habits) in a database before actual AGC execution. When AGC is needed, the system first queries this database for pre-stored gain settings matching current contextual parameters, avoiding the need to test the full gain range from scratch. This preliminary retrieval of cached results significantly reduces execution time while maintaining accuracy by using previously validated settings.
Solution Approach 2:
The system implements feedback mechanisms by continuously monitoring AGC protocol results and updating the database with new contextual data and optimal gain settings. The feedback loop analyzes historical performance data, user listening habits, and contextual factors to refine and optimize gain settings over time. This feedback-driven approach ensures that the system learns from past operations and progressively improves its gain selection accuracy while reducing the need for exhaustive testing.
2Reliability
If traditional AGC protocols test a full range of gain levels each time, then reliable gain determination is achieved, but processor resources are consumed
Solution Approach 1:
The system performs preliminary computation by pre-calculating and storing optimal gain settings in the database based on contextual parameters (media type, time of day, user preferences) before they are actually needed. When AGC execution is triggered, the system queries this pre-computed database for matching contextual conditions and retrieves the stored gain setting directly, bypassing the need for resource-intensive real-time gain range testing. This shifts processor workload from runtime computation to offline preparation.
Solution Approach 2:
The system creates and maintains copies of optimal gain settings in a database, organized by contextual parameters such as media type, time, and user habits. Instead of recalculating gain settings from scratch each time, the system copies and retrieves appropriate pre-determined gain values from this database based on current contextual matches. This copying approach preserves reliability by using validated settings while dramatically reducing processor resource consumption during actual AGC operations.
3Productivity
If historical data is used to adjust starting gain levels, then AGC protocol efficiency is improved, but system complexity increases
Solution Approach 1:
The system segments the AGC functionality into distinct modular components: a database module for storing contextual data and historical AGC results, a query module for retrieving matching settings based on current parameters, and an execution module for applying the retrieved gain settings. This segmentation allows each component to perform its specific function independently, making the overall system easier to manage and maintain despite the added complexity of historical data utilization. The modular architecture improves efficiency by enabling specialized optimization of each segment.
Solution Approach 2:
The system introduces a database as an intermediary layer between the AGC protocol execution and the gain determination process. This database intermediary stores and manages historical data, contextual information, and optimal gain settings, mediating between the input parameters and the AGC output. The intermediary simplifies complexity by centralizing data management and providing a structured interface for querying and retrieving pre-processed information, rather than embedding complex historical analysis logic directly within the AGC execution path.
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. Example apparatus disclosed herein are 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. The disclosed example apparatus are also to perform the AGC protocol based on the selected AGC parameter.