Adaptive Audio Coding Control Using Fuzzy Reinforcement Learning
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Multi-dimensional audio coding systems face challenges in dynamically adapting to varying performance goals due to complex configurations and interactions with different hardware platforms and environments, leading to difficulty in characterizing and controlling coding algorithms effectively.
Innovation Solution
An adaptive control mechanism using a controller that receives performance parameter values, calculates reward parameters, and employs reinforcement learning and fuzzy logic to select optimal coding tools and configurations in real-time, allowing the system to dynamically adjust for performance goals such as computational complexity, latency, and error control.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If multiple coding tools are selected to achieve different performance levels, then adaptability is improved, but device complexity increases
Solution Approach 1:
The patent implements dynamic configuration of audio coding tools through a controller that adapts the coding system in real-time based on performance parameters. The controller dynamically selects and configures coding tools from multiple options to achieve desired performance levels across different operating conditions, making the system adaptable without requiring complex manual configuration.
Solution Approach 2:
The audio coding system performs self-configuration through an automated controller that monitors performance parameters and autonomously adjusts coding tool selections. The system self-regulates its configuration based on feedback from performance measurements, eliminating the need for external intervention or complex user configuration while maintaining adaptability across different performance requirements.
2Adaptability or versatility
If the coding apparatus is configured to adapt to different hardware platforms and environments, then adaptability is improved, but difficulty of detecting and measuring increases
Solution Approach 1:
The patent implements a feedback mechanism where the controller continuously monitors performance parameters from the audio coding system and uses this feedback to adjust configurations. This closed-loop approach enables the system to adapt to different hardware platforms and environments by measuring actual performance and making data-driven configuration adjustments, simplifying the characterization process through empirical measurement rather than theoretical analysis.
Solution Approach 2:
The system adapts to different hardware platforms by dynamically changing operational parameters through the controller. Rather than characterizing the algorithm for each specific platform, the system measures performance parameters in real-time and adjusts coding tool configurations based on observed parameter values, making the system portable across platforms without requiring detailed prior knowledge of each platform's characteristics.
3Productivity
If real-time adaptation is implemented, then productivity is improved, but device complexity increases
Solution Approach 1:
The controller implements self-service by autonomously monitoring performance parameters and automatically adjusting coding tool configurations without external intervention. This automated self-configuration enables real-time adaptation to changing conditions, improving productivity by eliminating manual configuration time while the control complexity is encapsulated within the self-service mechanism rather than requiring complex external control systems.
Data Source
AI summary
An adaptive controller for a configurable audio coding system comprising a fuzzy logic controller modified to use reinforcement learning to create an intelligent control system. With no knowledge of the external system into which it is placed the audio coding system, under the control of the adaptive controller, is capable of adapting its coding configuration to achieve user set performance goals.