Adaptive Codebook Search Algorithm for Speech Coding
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Solution Overview
Problem
Existing coding technologies face challenges in reducing computation complexity while maintaining performance, particularly in searching for optimal pulse positions in algebraic codebooks, leading to high computation complexity and unstable performance across different signal conditions.
Innovation Solution
A coding method that selects different codebook search algorithms based on the characteristics of input signals, using low-complexity algorithms for signals with periodic characteristics and high-complexity algorithms for signals with white noise characteristics, thereby optimizing computation resources and ensuring coding quality.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a full search algorithm is used to search for optimal pulse positions in an algebraic codebook, then the coding quality is improved, but the computation complexity increases significantly
Solution Approach 1:
The patent divides the codebook search process into multiple stages: coarse search, fine search, and post-processing. The full search space is segmented into regions that are searched sequentially, with less critical regions searched first and more critical regions searched with higher precision. This segmentation allows the system to achieve good coding quality while avoiding the computational burden of a complete exhaustive search.
Solution Approach 2:
The patent applies different search strategies to different regions of the codebook based on their importance. Critical regions that have greater impact on coding quality are searched more thoroughly using finer granularity, while less critical regions use coarser search methods. This local quality approach ensures optimal performance is achieved where it matters most while reducing overall computation complexity.
2Device complexity
If a sub-optimal search algorithm is used to reduce computation complexity, then the computation complexity is reduced, but the coding quality deteriorates
Solution Approach 1:
The patent implements a dynamic search algorithm that adapts the search strategy based on signal characteristics and local conditions. The search granularity, depth, and focus are dynamically adjusted during the coding process based on the properties of the input signal and the current search state. This dynamic approach allows the system to maintain high coding quality while optimizing computation complexity for different signal types.
Solution Approach 2:
The patent incorporates feedback mechanisms where the results from coarse search stages inform the fine search stage, and the signal characteristics detected during searching guide subsequent search directions. This feedback loop allows the algorithm to learn from previous search results and concentrate computational resources on regions most likely to yield improvements in coding quality, thereby maintaining performance while reducing overall complexity.
3Device complexity
If the pulse positions are restrained on multiple tracks with fixed distribution, then the search complexity is reduced, but the adaptability to different signal types deteriorates
Solution Approach 1:
The patent makes the track structure and pulse distribution dynamic rather than fixed. The number of tracks, their positions, and the number of pulses per track are adjusted based on the characteristics of the input signal. This dynamic track configuration allows the search algorithm to adapt to different signal types (e.g., voiced vs. unvoiced speech) while maintaining manageable search complexity through structured organization.
Data Source
AI summary
A coding method is adapted to select different codebook search algorithms according to varied types of input signals. An encoder using the coding method is also provided. As appropriate search algorithms may be selected according to all possible structural features of the input signals, certain types of signals for which satisfactory results may be obtained through simple computations may match with search algorithms suitable for these signal types and having low computation complexities, so as to achieve better performance with fewer system resources. Meanwhile, other types of signals that need complicated computations may be processed by more sophisticated search algorithms, thereby ensuring the coding quality.