MDCT Window Function Shaping for Flexible Signal Reconstruction
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Solution Overview
Problem
Current audio and video coding systems face challenges in achieving flexible and computationally efficient window functions for MDCT applications, particularly in terms of passband selectivity and stopband rejection, with existing window functions either being too complex or offering limited design flexibility.
Innovation Solution
A signal processor that generates window functions using a weighted sum of a linear term and sine-type shaping functions, allowing for adjustable characteristics and efficient computation, which can produce a variety of window shapes with good energy conservation properties.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If predetermined window functions (sine, KBD, Vorbis) are used with MDCT, then perfect input reconstruction is achieved, but design flexibility and control over passband selectivity and stopband rejection are limited
Solution Approach 1:
The patent implements a dynamic window function generator that creates window functions adaptively based on signal characteristics and coding parameters. Instead of using fixed predetermined windows, the system dynamically adjusts window shape parameters (alpha, beta) and selects from multiple window types (Kaiser-Bessel, sine, custom) based on the specific coding situation, thereby achieving both design flexibility and reliable reconstruction through parameter adaptation rather than fixed designs
Solution Approach 2:
The patent changes the parameters of window functions dynamically during encoding and decoding processes. By adjusting parameters such as alpha (0-10), beta (0-10), and window length based on signal properties and rate control needs, the system can optimize passband selectivity and stopband rejection while maintaining perfect reconstruction through coordinated encoder-decoder parameter usage
2Productivity
If complex window functions (KBD, Sinha-Ferreira) are used to improve passband selectivity and stopband rejection, then coding efficiency increases, but computational complexity becomes prohibitive for real-time applications
Solution Approach 1:
The patent uses parameter changes to achieve high coding efficiency with lower computational complexity. By adjusting window parameters (alpha, beta, length) rather than using fixed complex window designs, the system can adapt to different coding scenarios and achieve optimal energy compaction without being locked into computationally expensive predetermined window functions
Solution Approach 2:
The dynamic window function generator allows the system to switch between different window types and parameter settings based on real-time signal characteristics and coding requirements. This dynamic adaptation enables the system to achieve high coding efficiency when needed while falling back to simpler computations when signal conditions permit, avoiding the constant high computational load of fixed complex windows
3Ease of manufacture
If fixed window functions are used, then implementation is simple, but the ability to adapt to different signal characteristics and optimize for specific coding scenarios is limited
Solution Approach 1:
The patent implements a dynamic window function selection and parameter adjustment mechanism that adapts to different signal characteristics automatically. The system analyzes signal properties and coding requirements to select appropriate window types and parameters, providing signal adaptability while maintaining implementation simplicity through automated decision-making rather than requiring manual configuration of complex adaptive systems
Solution Approach 2:
By changing window parameters dynamically based on signal characteristics, the patent achieves high signal adaptability without requiring completely different window functions for each scenario. The parameter adjustment mechanism allows a single implementation framework to handle diverse coding situations, maintaining simplicity while providing versatility
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.