Audio Encoder Gain Update Algorithm for Bit Allocation
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Solution Overview
Problem
Conventional audio encoding techniques face challenges in converging to an optimal gain value efficiently, leading to suboptimal bit allocation and quality issues, where the initial gain value may be too small or large, resulting in excessive iterations and potential distortion or insufficient bit usage.
Innovation Solution
The gain update process is adjusted to prioritize differences between encoded bit counts and allocated bits, allowing for more precise adjustment of the gain value to align with optimal bit usage, ensuring convergence to a suitable gain that balances bit allocation and audio quality.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If a predetermined gain change amount is used to update gain values, then the gain update process is simple to implement, but the convergence to optimal gain is slow and may fail to reach suitable gain values within the iteration limit
Solution Approach 1:
The patent changes the parameter of gain update from a fixed predetermined amount to a variable amount that adapts based on the current state. The gain change amount is dynamically adjusted according to the difference between current and target bit counts, allowing faster convergence when far from optimal and finer adjustment when approaching optimal gain values.
Solution Approach 2:
The gain update process transitions from a static predetermined approach to a dynamic adaptive approach. The system continuously adjusts the gain change magnitude based on real-time feedback from bit count measurements, making the update process responsive and efficient rather than rigid and slow.
2Reliability
If the initial gain value is set too small, then the encoding uses more bits than allocated, but requires many iterations to converge and may exceed the maximum iteration limit
Solution Approach 1:
The patent implements a feedback mechanism where the actual bit count from encoding is measured and used to adjust the gain change amount in subsequent iterations. This feedback loop allows the system to learn from previous encoding results and accelerate convergence by increasing the gain adjustment magnitude when bit counts indicate the need for larger changes.
Solution Approach 2:
The encoding system performs self-adjustment by using its own encoding output (bit count) to guide subsequent gain updates. The system serves itself by automatically determining the appropriate gain change magnitude based on its performance metrics, eliminating the need for external control or manual tuning.
3Reliability
If the initial gain value is set too large, then the encoding uses fewer bits than allocated, but requires many iterations to converge and may exceed the maximum iteration limit
Solution Approach 1:
The feedback mechanism works bidirectionally, adjusting gain upward or downward based on whether the bit count is below or above the target. When bits are underutilized, the system increases gain; when bits are overused, it decreases gain. The feedback-driven adaptive step size ensures efficient convergence from either direction.
4Productivity
If the gain update step size is increased to accelerate convergence, then fewer iterations are needed, but the risk of overshooting the optimal gain value increases
Solution Approach 1:
The gain update mechanism dynamically adjusts its step size based on the distance to the target bit count. Larger steps are taken when the system is far from optimal (accelerating convergence), while smaller steps are taken when approaching the target (ensuring precision). This dynamic adaptation resolves the contradiction between speed and accuracy.
Solution Approach 2:
The parameter of gain change magnitude changes from fixed to variable, allowing the system to optimize both convergence speed and accuracy. The adaptive parameter adjustment ensures that the update process is aggressive when needed for speed and conservative when needed for precision, automatically balancing the two requirements.
Data Source
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AI summary
A value of gain is updated so that the greater the difference between the number of bits or estimated number of bits in a code obtained by encoding a string of integer value samples obtained by dividing each sample in a sample string derived from an input audio signal in a given interval by gain before the update and a predetermined number B of allocated bits, the greater the difference between the gain before the update and the updated gain. A gain code corresponding to the updated gain and an integer signal code obtained by encoding a string of integer value samples obtained by dividing each sample in the sample string by the gain are obtained.