Delay Estimator Failure Detection for Acoustic Echo Cancellers
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing acoustic echo cancellers often fail due to unanticipated delays and noise, and previous detection methods are ineffective, especially during real-time communication sessions, as they rely on internal calculations that break down when the echo canceller logic fails.
Innovation Solution
The use of delay estimators to detect signal processing component failure by conveying a known audio signal to a loudspeaker and a post-processing delay estimator, calculating an estimated post-processing delay and confidence level, and comparing it to a threshold to determine if the acoustic echo canceller has failed, allowing for independent failure detection and potential adjustments or resets.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If internal performance metrics such as ERLE are calculated during acoustic echo canceller operation, then performance monitoring is enabled, but the detection method becomes ineffective when the echo canceller logic itself fails
Solution Approach 1:
The detection system is segmented into independent components: a known signal generator, a delay estimator, and a failure detector. These components operate separately from the acoustic echo canceller logic, allowing failure detection without relying on the canceller's internal state or calculations.
Solution Approach 2:
A delay estimator acts as an intermediary between the known signal generator and the failure detector. It measures the time delay of the known signal through the acoustic path and processing pipeline, providing an independent metric that reveals echo canceller failures without depending on internal performance metrics like ERLE.
2Productivity
If acoustic echo canceller operates under normal communication conditions, then real-time voice communication is enabled, but failure detection becomes difficult without separating echo from remaining audio signal
Solution Approach 1:
A known signal is injected into the acoustic path before the echo canceller processes normal communication audio. This preliminary action creates a detectable reference that persists through the processing pipeline, enabling failure detection during real-time communication without requiring echo separation or constrained conditions.
Solution Approach 2:
The system changes the parameter of the test signal from random noise to a known deterministic signal with identifiable characteristics. This parameter change allows the delay estimator to reliably detect the signal's presence and measure its delay, enabling failure detection during normal communication operations.
3Adaptability or versatility
If additional delay is introduced by wireless communication networks or interconnecting cables, then system connectivity and flexibility are improved, but acoustic echo canceller effectiveness is reduced or eliminated
Solution Approach 1:
The delay estimator provides feedback about the actual time delay experienced by audio signals through the system. This feedback includes delays introduced by wireless networks or cables, allowing the system to adapt and maintain reliable echo cancellation effectiveness despite varying connection conditions.
Data Source
AI summary
One or more delay estimators are used to detect failure in a signal processing component, such as an acoustic echo canceller. A first delay estimator is used to generate i) an estimated post-processing delay between when a known audio signal was conveyed to a loudspeaker, and when a portion of the processed audio signal that includes the known audio signal was output from the signal processing component, and ii) a confidence level for the estimated post-processing delay. A failure of the signal processing component may be detected in response to the estimated post-processing delay exceeding a threshold. A second delay estimator may also be used to generate an estimated pre-processing delay and a confidence level for the estimated pre-processing delay for comparison to the estimated post-processing delay and confidence level for the estimated post-processing delay in order to provide further failure detection accuracy and specificity.