Frequency-Domain Adaptive Filter Stability Under Large Signal Ranges
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Solution Overview
Problem
Adaptive filters, particularly those operating in the frequency domain, face instability issues due to large dynamic ranges of input signals, leading to potential divergence and instability, especially in applications like audio processing where speech and music are involved.
Innovation Solution
The adaptive filter dynamically adjusts stability conditional numbers and noise floors based on peak magnitudes of frequency domain signals to control step size normalization and selectively update coefficients, ensuring stability and efficient adaptation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If frequency domain adaptive filter is used to achieve fast convergence, then convergence speed is improved, but stability deteriorates due to large dynamic range of input signals
Solution Approach 1:
The patent implements dynamic adjustment of the stability conditional number based on the actual dynamic range of the input signal's frequency domain components. Instead of using a fixed stability parameter, the system continuously monitors the signal characteristics and adapts the stability conditional number in real-time, allowing the filter to maintain optimal performance across varying signal conditions without sacrificing stability.
Solution Approach 2:
The patent changes the stability parameter (stability conditional number) from a fixed value to a dynamically adjustable parameter that adapts to signal characteristics. By modifying this parameter based on the observed dynamic range of frequency domain components, the system resolves the contradiction between fast convergence and stability, enabling the filter to converge quickly when conditions permit while maintaining stability when signal variations are large.
2Stability of the object's composition
If step size normalization is applied to control coefficient adaptation, then stability is improved, but convergence speed deteriorates
Solution Approach 1:
The patent makes the step size normalization dynamic by adjusting it based on the stability conditional number, which itself is adapted to the signal's dynamic range. This dynamic approach allows the step size to be normalized conservatively when signal variations are large (maintaining stability) and more aggressively when signal conditions are stable (improving convergence speed), thus resolving the contradiction between stability and convergence speed.
Solution Approach 2:
The patent changes the step size normalization parameter from a static value to one that is dynamically adjusted based on the stability conditional number and signal characteristics. This parameter adaptation enables the system to optimize the trade-off between stability and convergence speed by modifying the normalization factor according to actual operating conditions, rather than using a fixed conservative value.
3Reliability
If fixed stability conditional number is used to ensure stability, then reliability is improved, but adaptability deteriorates
Solution Approach 1:
The patent transforms the fixed stability conditional number into a dynamic parameter that adapts to different signal conditions. By continuously monitoring the dynamic range of frequency domain components and adjusting the stability conditional number accordingly, the system maintains reliability across varying operating conditions while gaining the adaptability to optimize performance for different types of input signals, thus resolving the contradiction between reliability and adaptability.
Solution Approach 2:
The patent changes the stability conditional number from a fixed parameter to an adaptive parameter that varies with signal characteristics. This parameter change enables the system to maintain the reliability benefits of stability control while gaining adaptability to different signal scenarios, allowing optimal performance across diverse applications such as audio processing, noise cancellation, and speech enhancement.
Data Source
AI summary
An adaptive filter converts time domain samples of an input signal into frequency domain signals, dynamically adjusts a stability conditional number based on the frequency domain signals, and uses the dynamically adjusted stability conditional number to control step size normalization during adaptation of frequency domain coefficients of the adaptive filter. The stability control number may be global to a range of frequency bins based on a peak magnitude of the input signal and/or may be frequency bin-specific stability control numbers based on corresponding frequency bin-specific error signal magnitudes. The adaptive filter also dynamically adjusts a noise floor based on the frequency domain input signals and refrains from updating frequency domain coefficients when a magnitude of the frequency domain input signal associated with a frequency bin is greater than the dynamically adjusted noise floor.