Polyphase Audio Resampling Filters for Low-Complexity Alias Control
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Solution Overview
Problem
Existing audio resampling technologies face challenges in efficiently reducing computational complexity and minimizing audible artifacts when resampling audio signals at rational sampling rates, leading to unwanted imaging and aliasing artifacts.
Innovation Solution
A system and method for implementing anti-imaging/anti-aliasing filters using a polyphase structure with look-up tables to determine current subsets of filter coefficients, reducing computational complexity by exploiting the polyphase structure and eliminating unnecessary operations, and optimizing the determination of phase indexes and filter coefficients for efficient resampling.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Object-affected harmful factors
If traditional anti-imaging/anti-aliasing filters are used for rational resampling, then imaging and aliasing artifacts are suppressed, but computational complexity increases
Solution Approach 1:
The filter is divided into multiple polyphase components (e.g., L phases for upsampling factor L), where each phase processes a subset of input samples. This segmentation allows the filter to operate at a lower effective rate, reducing computational complexity while maintaining artifact suppression performance.
Solution Approach 2:
Filter coefficients are pre-calculated and stored in look-up tables organized by phase index. The system pre-determines which coefficient subsets to use for each phase, eliminating the need for real-time coefficient calculation and reducing computational overhead during resampling operations.
2Measurement precision
If filter coefficients are calculated in real-time, then accuracy is maintained, but computational overhead increases
Solution Approach 1:
All filter coefficients are pre-calculated offline and stored in look-up tables. During runtime, the system only performs table lookups based on the current phase index, maintaining coefficient accuracy while reducing computational overhead from real-time calculations to simple memory access operations.
Solution Approach 2:
The system uses look-up tables that store pre-computed coefficient subsets for each phase. Instead of recalculating coefficients, the system copies appropriate coefficient subsets from the look-up tables based on the current phase index, ensuring accuracy while minimizing computational effort.
3Productivity
If polyphase structure is implemented without optimization, then resampling efficiency is improved, but memory access complexity increases
Solution Approach 1:
The system pre-determines and stores the sequence of phase indexes in a look-up table. This allows the polyphase filter to efficiently cycle through phases using simple table lookups rather than complex real-time phase sequence calculations, improving resampling efficiency while keeping memory access patterns simple and predictable.
Data Source
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AI summary
The present document relates to the design of anti-aliasing and/or anti-imaging filters for resamplers using rational resampling factors. In particular, the present document relates to a method for implementing such anti-aliasing and/or anti-imaging filters with reduced computational complexity. In addition, the present document relates to further aspects of an audio encoding and decoding system, such as the phase relation between the channels of a multi-channel audio signal and/or the structure of the bitstream of an encoded audio signal.