Adaptive Time-Domain Filter Coefficients for Low-Latency Audio
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Solution Overview
Problem
Time domain filtering in audio signal processing faces challenges in adapting filter coefficients in real-time due to the time-varying nature of audio signals, leading to suboptimal frequency response and increased latency.
Innovation Solution
Adaptive filter coefficient updating using a least squares method, where target gains are obtained for each audio subband, and filter coefficients are iteratively updated based on a predetermined mapping matrix, allowing for real-time adjustment of filter coefficients to match changing audio characteristics.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If time domain filtering is used to achieve ultra-low latency and improved linear convolution, then processing speed and convolution quality are improved, but the filter coefficients cannot be adapted in real-time due to the time-varying nature of audio signals
Solution Approach 1:
The patent applies dynamics by transitioning from static pre-trained filter coefficients to dynamic adaptive filter coefficients that change in real-time. The filter coefficients are continuously updated based on the current audio signal characteristics through iterative optimization, allowing the filter to adapt to time-varying audio signals while maintaining ultra-low latency time domain processing.
Solution Approach 2:
The patent changes the parameters of the filter system by adjusting filter coefficients in real-time based on audio signal characteristics. Instead of using fixed pre-trained coefficients, the system iteratively optimizes coefficients to match target frequency responses, enabling adaptation to different audio conditions while preserving the speed advantages of time domain filtering.
2Adaptability or versatility
If filter coefficients are adapted in real-time to match time-varying audio signals, then adaptability is improved, but latency increases and processing efficiency decreases
Solution Approach 1:
The patent applies partial action by performing adaptive coefficient updates only when necessary, based on changes in audio signal characteristics. Rather than continuously retraining filters, the system iteratively adjusts coefficients in response to significant signal variations, achieving adaptability while minimizing the time loss associated with coefficient updates.
Solution Approach 2:
The patent uses preliminary action by pre-training filters offline to obtain initial filter coefficients before real-time processing. This pre-training establishes a baseline that reduces the computational burden during real-time operation, allowing the system to adapt to time-varying signals with minimal latency by making incremental adjustments from the pre-trained state.
3Ease of manufacture
If pre-trained offline filters with fixed coefficients are used, then device complexity is reduced and ease of operation is improved, but the frequency response cannot adapt to time-varying audio signals
Solution Approach 1:
The patent transforms the static filter design into a dynamic system where coefficients are automatically adjusted in real-time. The simplicity of using pre-trained filters is preserved for initial setup, but the system gains adaptability through iterative coefficient updates that automatically track target frequency responses based on current audio signal characteristics.
Solution Approach 2:
The patent implements self-service by enabling the filter system to automatically adapt its own coefficients without requiring manual retraining or complex offline processing. The system uses iterative optimization algorithms to self-adjust coefficients based on audio signal analysis, maintaining ease of operation while achieving frequency response adaptability to time-varying signals.
Data Source
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AI summary
Example embodiments disclosed herein relate to filter coefficient updating in time domain filtering. A method of processing an audio signal is disclosed. The method includes obtaining a predetermined number of target gains for a first portion of the audio signal by analyzing the first portion of the audio signal. Each of the target gains is corresponding to a subband of the audio signal. The method also includes determining filter coefficients for time domain filtering the first portion of the audio signal so as to approximate a frequency response given by the target gains. The filter coefficients are determined by iteratively selecting at least one target gain from the target gains and updating the filter coefficient based on the selected at least one target gain. Corresponding system and computer program product for processing an audio signal are also disclosed.