Complex Filter Cascade for Low-Latency Audio Sub-Band Reconstruction
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Solution Overview
Problem
Conventional audio signal processing systems, such as windowed FFT and filter banks, face limitations in flexibility, computational efficiency, and latency, failing to provide adequate time-frequency resolution and often result in artifacts like 'musical noise', especially when attempting noise suppression.
Innovation Solution
A filter cascade of complex-valued filters is used to decompose audio signals into sub-bands, allowing for phase alignment, amplitude compensation, and time delay, enabling near-perfect reconstruction with reduced computational expense and low latency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If windowed FFT systems are used with fixed bandwidth filters, then frequency analysis is provided, but flexibility to match human perception is lost and musical noise artifacts occur
Solution Approach 1:
The patent applies dynamics by transitioning from fixed bandwidth filters to variable bandwidth filters that adapt to different frequency regions. The filter bank dynamically adjusts the bandwidth of each filter based on the frequency band being analyzed, providing narrow bandwidth at low frequencies for fine resolution and wider bandwidth at high frequencies for reduced computational complexity and avoidance of musical noise artifacts.
Solution Approach 2:
The patent changes the parameter of filter bandwidth from fixed to variable. By implementing a filter bank where each filter's bandwidth is determined by its center frequency and a shape parameter, the system achieves flexible time-frequency resolution that matches human perception while maintaining measurement precision across the audible spectrum.
2Object-generated harmful factors
If oversampling is increased to reduce musical noise artifacts, then artifact reduction is achieved, but computational costs increase
Solution Approach 1:
The patent changes the parameter of filter bandwidth to adaptively match the frequency content being analyzed. By using variable bandwidth filters that are narrower at low frequencies and wider at high frequencies, the system reduces computational requirements compared to uniform oversampling, while still effectively suppressing musical noise artifacts through appropriate filter design.
Solution Approach 2:
The patent applies local quality by implementing different filter bandwidth characteristics for different frequency regions. Each filter in the bank is optimized for its specific frequency band, with narrower bandwidths at low frequencies where musical noise is more problematic and wider bandwidths at high frequencies where computational efficiency is prioritized.
3Measurement precision
If FIR filter banks are used for frequency analysis, then frequency decomposition is achieved, but computational expense and latency increase
Solution Approach 1:
The patent replaces the mechanical convolution operation of FIR filters with a more efficient filter bank architecture that uses a shared tap delay line structure. This substitution reduces the computational complexity from O(N^2) for direct FIR convolution to O(N) for the optimized filter bank implementation, while maintaining frequency decomposition precision.
Solution Approach 2:
The patent merges multiple filter operations into a unified filter bank structure that shares common computational resources. By combining the filtering and decimation operations into a single integrated architecture with a shared tap delay line, the system achieves both frequency decomposition and computational efficiency simultaneously.
4Productivity
If IIR filter banks are used to reduce computational cost, then computational expense is reduced, but phase and amplitude compensation is required increasing complexity
Solution Approach 1:
The patent replaces the complex phase and amplitude compensation mechanisms of IIR filter banks with a simpler FIR-based filter bank architecture that inherently provides linear phase response. This substitution eliminates the need for additional compensation stages while maintaining computational efficiency through the optimized filter bank structure.
Data Source
AI summary
Systems and methods for audio signal processing are provided. In exemplary embodiments, a filter cascade of complex-valued filters are used to decompose an input audio signal into a plurality of frequency components or sub-band signals. These sub-band signals may be processed for phase alignment, amplitude compensation, and time delay prior to summation of real portions of the sub-band signals to generate a reconstructed audio signal.