QMF Filter Compression for Long Impulse Response Audio Filtering
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Solution Overview
Problem
The computational complexity of filtering in the Quadrature Mirror Filterbank (QMF) domain, particularly with long impulse responses such as HRTF filters, is substantial, leading to high computational demands and potential audible distortions due to the need for extensive calculations and long filter lengths.
Innovation Solution
A method to reduce computational complexity by selecting and compressing filter impulse responses, where less significant coefficients are set to zero based on their absolute values or aliasing levels, allowing for parallel processing in the subband domain while maintaining audio quality by preserving key coefficients.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If long impulse response filters are used in the QMF domain to model room characteristics, then audio quality is improved, but computational complexity increases substantially
Solution Approach 1:
The filter impulse response is divided into multiple subbands in the QMF domain, allowing parallel processing of shorter filter segments instead of one long filter, thus reducing computational complexity while maintaining audio quality
Solution Approach 2:
The filter coefficients are transformed from the time domain to the QMF domain, changing the representation parameters to enable more efficient processing with reduced computational requirements
2Device complexity
If filter coefficients are compressed by setting less significant coefficients to zero, then computational complexity is reduced, but filtering precision may deteriorate
Solution Approach 1:
Different coefficients are treated differently based on their significance - important coefficients are preserved while less significant ones are set to zero, optimizing the balance between precision and complexity
Solution Approach 2:
Instead of processing all coefficients equally, only the most significant coefficients are retained and processed, achieving sufficient filtering precision with reduced computational effort
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.