Analysis Filter Bank Using Binomial Combiners for Real-Time Audio Shifting
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Solution Overview
Problem
Existing frequency shifting systems face challenges in achieving high-quality, real-time audio processing with low computational complexity, particularly on low-power devices, due to the complexity of high-order filtering and single-sideband conversion operations, which limits their implementation on mobile and wearable devices.
Innovation Solution
An analysis filter bank design with first-order IIR filtering and binomial combiners is employed to generate fine spectrums for dynamic frequency shifting, reducing computational complexity while maintaining audio quality, suitable for real-time software implementation on low-power devices.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If high-order filtering and single-sideband conversion operations are used in frequency shifting systems, then audio processing quality is improved, but computational complexity increases
Solution Approach 1:
The audio signal is divided into multiple sub-bands using a filter bank structure, where each sub-band is processed independently with simplified first-order IIR filters. This segmentation allows the system to achieve frequency-selective processing without requiring complex high-order filters applied to the entire signal, thus reducing computational complexity while maintaining audio quality.
Solution Approach 2:
The patent replaces traditional mechanical analog filter designs with digital first-order IIR filters combined with binomial combiners. This substitution uses mathematical computation instead of complex physical filtering mechanisms, achieving frequency separation with significantly reduced computational requirements compared to conventional high-order digital filters.
2Manufacturing precision
If conventional frequency shifting algorithms are implemented, then audio quality is maintained, but processing delay increases
Solution Approach 1:
The system uses periodic binomial combining operations at regular intervals within each sub-band to achieve frequency shifting. This periodic approach allows for efficient real-time processing with minimized delay, as the combining operations are performed at optimized intervals rather than requiring continuous complex computations.
3Productivity
If Rollers frequency shifting algorithm is used, then real-time processing with low delay is achieved, but device power consumption increases
Solution Approach 1:
The patent changes the filter order parameter from high-order to first-order IIR filters, and modifies the combining approach to use binomial coefficients. These parameter changes dramatically reduce the number of multiplications and additions required per sample, enabling real-time processing on low-power mobile and wearable devices while maintaining the low-latency characteristics of the Rollers algorithm.
Data Source
AI summary
An analysis filter bank corresponding to a plurality of sub-bands, comprising: multiple sub-filters with different center frequencies which perform multiple complex-type first-order infinite impulse response filtering operations on an audio input signal to generate multiple sub-filter signals; a first set of binomial combiners, each of which performs a weighted-sum operation on a first number of the sub-filter signals with a first set of binomial weights to generate one of multiple sub-band signals; a second set of binomial combiners, each of which performs a weighted-sum operation on a second number of the sub-filter signals with a second set of binomial weights to generate one of multiple lower sub-band-edge signals or one of multiple higher sub-band-edge signals; and multiple envelope detection with decimation devices, which perform multiple envelope detection with decimation operations on the sub-band signals, the lower sub-band-edge signals, and the higher sub-band-edge signals to generate multiple fine spectrums.