Signal Window Shaping for Low-Complexity Audio Coding
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Solution Overview
Problem
Current signal processing technologies, particularly in audio and video coding systems, face challenges in achieving flexible and computationally efficient window functions that balance passband selectivity and stopband rejection, with existing methods like KBD and Sinha-Ferreira windows being computationally expensive and offering limited flexibility.
Innovation Solution
The development of a signal processor that determines window values through a weighted summation of linear terms and shaping functions, allowing for adjustable window characteristics with low computational effort, using sine functions and point-symmetric shaping functions to achieve good energy conservation and compaction characteristics.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If KBD or Sinha-Ferreira window functions are used to improve passband selectivity and stopband rejection, then filtering performance is improved, but computational complexity increases significantly
Solution Approach 1:
The window function is segmented into multiple components (KBD window portion and sine window portion) that can be independently controlled. This allows the system to use only the necessary computational resources for the current signal characteristics, rather than always computing the full complex window function.
Solution Approach 2:
The window function dynamically switches between different configurations based on signal characteristics. The mixing parameter α allows continuous adjustment between pure KBD (α=1) and pure sine (α=0) windows, enabling the system to adapt computational complexity to the actual filtering needs of each signal segment.
2Device complexity
If fixed window functions are used to simplify the system, then device complexity is reduced, but adaptability to different signal characteristics is limited
Solution Approach 1:
The system changes the parameter α to adapt to different signal characteristics. By adjusting α between 0 and 1, the same window function structure can optimize for different signal types (e.g., transient vs. steady-state signals) without requiring multiple fixed window functions or complex adaptive algorithms.
Solution Approach 2:
The window function serves multiple functions through a single unified structure. It can operate as a pure KBD window, pure sine window, or any combination therebetween, making it universally applicable to different signal processing scenarios while maintaining a single implementation framework.
3Loss of information
If complex window functions are used to improve energy compaction, then coding gain is increased, but processing time increases
Solution Approach 1:
The system applies partial action by using only the necessary portion of the window function for each signal segment. When signal characteristics don't require full KBD window complexity, the system uses a reduced version (lower α value), saving processing time while maintaining sufficient energy compaction for the given signal type.
Data Source
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AI summary
A signal processor for providing a processed version of an input signal in dependence on the input signal comprises a windower configured to window a portion of the input signal, or of a pre-processed version thereof, in dependence on a signal processing window described by signal processing window values for a plurality of window value index values, in order to obtain the processed version of the input signal. The signal processor also comprises a window provider for providing the signal processing window values for a plurality of window value index values in dependence on one or more window shape parameters.