Filter Parameter Indexing for Low-Overhead Audio Equalization
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Solution Overview
Problem
Existing audio signal processing technologies in virtual and augmented reality systems face challenges in efficiently controlling magnitude responses of filters, particularly in dynamic environments, due to high computational requirements and resource constraints.
Innovation Solution
A system and method using a cascade of shelving filters to create a 3-band parametric equalizer, with gain values derived from prototype filter parameters and stored in a lookup table, allowing for efficient retrieval and interpolation of magnitude responses, reducing computational overhead through an indexing scheme.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a proportional parametric equalizer with cascade of shelving filters is used for accurate magnitude response control, then audio signal processing accuracy is improved, but computing cycles and resources increase significantly
Solution Approach 1:
The patent pre-computes and stores magnitude response data in lookup tables before runtime. The cascade of shelving filters is analyzed offline to generate pre-calculated magnitude response values that are stored in memory. During actual audio processing, the system retrieves pre-computed values from the lookup table based on filter parameters, avoiding the need to perform complex filter calculations in real-time. This preliminary computation resolves the contradiction by sacrificing offline computation time to gain significant runtime efficiency.
Solution Approach 2:
The patent creates simplified copies of the complex filter processing by storing magnitude response data in lookup tables. Instead of executing the full cascade of shelving filters during audio processing, the system uses pre-computed lookup table entries that replicate the filter's magnitude response characteristics. This copying approach maintains accurate magnitude response control while dramatically reducing the computational burden during runtime.
2Productivity
If filter magnitude response data is pre-computed and stored in lookup table, then runtime processing speed is improved, but storage memory requirements increase
Solution Approach 1:
The patent segments the magnitude response data into discrete lookup table entries corresponding to specific filter parameter combinations. Instead of storing complete filter responses for all possible parameter values, the system divides the parameter space into discrete segments and pre-computes magnitude response data only for these segmented points. During runtime, the system retrieves data from these segmented lookup table entries and interpolates as needed, reducing overall memory requirements while maintaining processing speed.
Solution Approach 2:
The patent optimizes memory usage by selectively pre-computing and storing lookup table entries for the most frequently used or critical filter parameter combinations. Rather than uniformly storing data for all possible parameter values, the system identifies and prioritizes specific parameter ranges and configurations that are most relevant to the application, storing detailed magnitude response data for these while using coarser representations or interpolation for less critical parameters.
3Productivity
If lookup table is accessed for each filter data retrieval, then computational overhead at runtime is reduced, but access time and memory bandwidth usage increase
Solution Approach 1:
The patent merges multiple filter parameter variations into a single unified lookup table structure. Instead of maintaining separate lookup tables for different filter configurations or parameter ranges, the system consolidates magnitude response data for multiple filter types and parameter combinations into one integrated table. This merging reduces the number of separate memory access operations required and improves cache utilization, offsetting the increased access time per entry through reduced access frequency.
Solution Approach 2:
The patent designs the lookup table with universal applicability across multiple filter configurations and parameter ranges. A single lookup table structure serves multiple functions by storing magnitude response data that can be used for different filter types, parameter combinations, and even different sampling rates. This multi-functionality reduces the total number of lookup table accesses needed during runtime, as the same table can service multiple different query types without requiring separate specialized tables.
Data Source
AI summary
A method of processing an audio signal is disclosed. According to embodiments of the method, magnitude response information of a prototype filter is determined. The magnitude response information includes a plurality of gain values, at least one of which includes a first gain corresponding to a first frequency. The magnitude response information of the prototype filter is stored. The magnitude response information of the prototype filter at the first frequency is retrieved. Gains are computed for a plurality of control frequencies based on the retrieved magnitude response information of the prototype filter at the first frequency, and the computed gains are applied to the audio signal.