EOC System Voice Echo Suppression via Signal Analysis
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Solution Overview
Problem
Feed-forward Engine Order Cancellation (EOC) systems face mis-adaptation issues due to non-stationary events like speech and transient noise, leading to suboptimal noise cancellation performance as they adapt to these events, causing temporary degradation in noise reduction effectiveness.
Innovation Solution
Implementing a method to detect non-stationary events through signal parameter analysis, using voice activity detectors, and modifying adaptation parameters to reduce the rate of filter adaptation or pause filter adjustments during such events, thereby preventing mis-adaptation and maintaining optimal noise cancellation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the LMS EOC system continuously adapts the W-filters based on error microphone signals, then the system can optimally cancel engine order frequencies, but it will mis-adapt to transient non-stationary events like speech or road imperfections, causing temporary degradation in noise cancellation performance
Solution Approach 1:
The system performs preliminary detection of non-stationary events (speech, road imperfections) before they can cause mis-adaptation. By detecting these events in advance and pausing filter adaptation preemptively, the system prevents the W-filters from adapting to transient events that would degrade noise cancellation performance.
Solution Approach 2:
The system uses feedback from the error microphone signals to detect non-stationary events and adjusts the adaptation process accordingly. The detected events trigger a pause in filter adaptation, creating a feedback loop that prevents mis-adaptation while maintaining optimal noise cancellation when conditions are stable.
2Ease of operation
If the W-filters are adapted based on transient non-stationary events, then the filters adjust to these events, but the EOC system needs to re-adapt for a period of time after the events, worsening the overall noise cancellation
Solution Approach 1:
The system pauses filter adaptation in advance when non-stationary events are detected, preventing the time loss associated with re-adaptation. By stopping adaptation before the transient event fully impacts the filters, the system avoids the need for lengthy re-adaptation periods and maintains continuous optimal performance.
3Measurement precision
If the system uses multiple microphones to detect non-stationary events, then detection accuracy improves, but the system complexity increases
Solution Approach 1:
The system uses an intermediary detection mechanism that analyzes error microphone signals to identify non-stationary events. By using the existing error microphone signals as intermediaries and applying signal processing techniques to detect anomalies, the system achieves accurate detection without requiring a complex array of additional microphones.
Data Source
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AI summary
Engine order cancellation (EOC) systems generate feed forward noise signals based on the engine or other rotating shaft RPM and use those signals and adaptively configured W-filters to reduce the in-cabin SPL by radiating anti-noise through speakers. An EOC system may include a signal analysis controller for detecting non-stationary events, such as speech, based on parameters sampled from a current frame of error signals output from microphones positioned in various locations of a vehicle passenger cabin. Upon detection, the signal analysis controller may mitigate the effects of the non-stationary event to prevent the EOC system from boosting noise or contributing to a speech-like post-echo in the passenger cabin. For example, if speech is detected in a frame, then the adaptation can be frozen for that frame. Alternatively, the signal analysis controller may adaptively subtract voice signals out of the error microphone signal.