Adaptive Filter Coefficient Resolution for Binaural Audio Bit-Rate Reduction
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Solution Overview
Problem
Current digital signal processing technologies face challenges in efficiently handling filter coefficients, particularly in mobile devices with limited processing power and high bit-rate requirements for effective binaural surround sound rendering, which limits the quality of 3D audio perception.
Innovation Solution
The method involves adaptively tuning and reducing the representation of filter coefficients based on frequency response, using binaural processing bands to efficiently handle filter coefficients, and storing them in reduced form to minimize storage and computational complexity, allowing for efficient binaural rendering with lower bit-rates.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If complete 3D audio rendering with high-quality HRF filtering is used, then the quality of 3D audio perception is improved, but the bit-rate requirement increases significantly
Solution Approach 1:
The patent segments the frequency spectrum into multiple bands (e.g., 32 or 64 bands) and applies different filtering strategies to each band. In lower frequency bands where HRF effects are less critical, fewer filter coefficients are used, while higher frequency bands maintain higher resolution. This segmentation allows reducing overall bit-rate while preserving perceptual quality where it matters most.
Solution Approach 2:
The patent applies local quality by using full-resolution HRF filter coefficients only in frequency bands where they are most needed (higher frequencies), while using reduced-resolution or simplified filters in lower frequency bands. This localized approach optimizes the trade-off between audio quality and bit-rate by concentrating computational resources where they provide the most perceptual benefit.
2Measurement precision
If high-resolution filter coefficients are stored and processed, then the accuracy of binaural rendering is improved, but the processing power requirement increases
Solution Approach 1:
The patent divides the filter processing into multiple frequency bands, each handled separately with appropriate resolution. This segmentation reduces the computational complexity of any single filtering operation while maintaining overall accuracy through the combination of band-specific results.
Solution Approach 2:
The patent applies different levels of filtering accuracy locally across frequency bands, using high-precision filters only where necessary for accurate binaural rendering (higher frequencies), and simplified filters where lower precision is acceptable (lower frequencies). This reduces total processing power requirements while maintaining rendering accuracy where it matters.
3Measurement precision
If full-resolution filter coefficients are stored, then the fidelity of impulse response representation is improved, but the storage requirement increases
Solution Approach 1:
The patent segments the filter coefficient storage into frequency bands, storing full-resolution coefficients only for bands where high fidelity is critical (higher frequencies), and compressed or simplified representations for lower frequency bands. This reduces total storage requirements while maintaining impulse response fidelity where it impacts perception most.
Solution Approach 2:
The patent applies local quality by storing high-fidelity filter coefficients selectively in frequency regions where they are most needed for accurate binaural rendering, rather than uniformly across all frequencies. This optimized storage strategy reduces memory requirements while preserving the essential characteristics of the impulse response.
Data Source
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AI summary
In signal processing using digital filtering the representation of a filter is adapted depending on the filter characteristics. If e.g. a digital filter is represented by filter coefficients for transform bands Nos. 0, 1,..., K in the frequency domain, a reduced digital filter having coefficients for combined transform bands, i.e. subsets of the transformed bands, Nos. 0, 1,..., L, is formed and only these coefficients are stored. When the actual filtering in the digital filter is to be performed, an actual digital filter is obtained by expanding the coefficients of the reduced digital filter according to a mapping table and then used instead of the original digital filter.