Subband Filter Compression for Long HRTF Audio Processing
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Solution Overview
Problem
Existing audio filtering technologies face significant computational complexity, especially when dealing with long impulse response filters like HRTF filters, which hinder efficient processing and increase computational demands.
Innovation Solution
A filter compressor system that converts time-domain filters into the subband domain, selectively reducing filter impulse response values and setting lower values to zero, thereby reducing computational complexity while maintaining audio quality by constructing compressed subband filter impulse responses.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If long impulse response filters (e.g., HRTF filters) are used to model room characteristics, then audio quality and realism are improved, but computational complexity increases significantly
Solution Approach 1:
The filter impulse response is divided into multiple subbands in the frequency domain, allowing selective processing of different frequency components. This segmentation enables the system to maintain audio quality by preserving important frequency components while reducing computational complexity by processing only significant subbands with non-negligible energy.
Solution Approach 2:
Different processing strategies are applied to different subbands based on their energy characteristics. Subbands with high energy are processed with higher precision to maintain audio quality, while subbands with low energy are processed more coarsely or discarded, optimizing the balance between quality and computational complexity.
2Device complexity
If filter impulse response values are reduced by setting lower values to zero, then computational complexity is reduced, but filtering accuracy may deteriorate
Solution Approach 1:
The filter coefficients are modified in the frequency domain by selectively zeroing out coefficients with negligible energy contribution. This parameter change approach allows the system to reduce computational complexity while maintaining filtering accuracy for the most important frequency components, as the zeroing operation is performed based on energy thresholds that preserve audio quality.
3Productivity
If computational complexity is reduced by processing fewer filter coefficients, then processing speed improves, but audio quality may be compromised
Solution Approach 1:
Instead of processing all filter coefficients equally, the system applies partial processing by focusing computational resources on the most significant subbands and coefficients. This selective processing approach achieves the necessary audio quality with reduced computational effort, as the energy distribution in audio signals is typically concentrated in specific frequency regions.
Data Source
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AI summary
A filter system comprises a filter converter (101) and a filter compressor (102) for generating compressed subband filter impulse responses from input subband filter impulse responses corresponding to subbands, which comprise filter impulse response values at filter taps. The filter compressor comprises a processor (820) for examining the filter impulse response values from at least two input subband filter input responses to find filter impulse response values having higher values and at least one filter impulse response value having a value being lower than the higher values, and a filter impulse response constructor (305) for constructing the compressed subband filter impulse responses using the filter impulse response values having the higher values, wherein the compressed subband filter impulse responses do not include filter impulse response values corresponding to filter taps of the at least one filter impulse response value having the lower value or comprise zero-valued values corresponding to filter taps of the at least one filter impulse response value having the lower value.