Residual Echo Suppression for Spatial Audio Transitions
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Solution Overview
Problem
Existing audio processing systems face challenges in effectively canceling and suppressing residual echo, particularly during transitions from spatially quiescent to spatially rich audio scenarios, where conventional echo cancellation methods often fail to adequately remove unwanted echoes.
Innovation Solution
The system employs a residual echo suppression method that estimates echo levels using echo return loss and echo return loss enhancement trackers, and adjusts its suppression techniques based on prediction error levels and offsets, allowing for effective echo removal even without modifying audio signals intended for output, and is transparent during periods of spatial quiescence and richness.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional echo cancellation methods are used, then echo suppression is maintained during spatially quiescent periods, but residual echo removal fails during transitions to spatially rich scenarios
Solution Approach 1:
The system dynamically adjusts echo cancellation parameters based on the spatial characteristics of the audio signal. During spatially quiescent periods, standard echo cancellation is applied. When transitioning to spatially rich scenarios, the system detects the change and adapts the cancellation parameters to maintain effectiveness, preventing residual echo issues.
Solution Approach 2:
The system continuously monitors the spatial characteristics of the audio signal and uses this feedback to adjust echo cancellation parameters in real-time. This feedback mechanism allows the system to detect transitions between spatially quiescent and spatially rich states and respond appropriately to maintain reliable echo suppression.
2Manufacturing precision
If echo cancellation parameters are adjusted for spatially rich scenarios, then residual echo removal improves, but performance degrades during spatially quiescent periods
Solution Approach 1:
The system uses dynamic parameter adjustment based on detected spatial characteristics. During spatially quiescent periods, standard parameters are used to maintain reliability. When spatially rich scenarios are detected, parameters are adapted to improve residual echo removal precision, with the system switching between these modes based on real-time spatial analysis.
3Adaptability or versatility
If multi-channel audio processing is implemented, then spatial audio quality improves, but echo cancellation complexity increases
Solution Approach 1:
The system processes different audio channels separately, analyzing spatial characteristics for each channel independently. This segmentation allows the system to handle multi-channel audio while managing complexity through channel-by-channel processing rather than attempting to process all channels simultaneously as a single complex unit.
Data Source
AI summary
A system performs residual echo suppression on a microphone signal that receives, e.g., voice commands, and that is exposed to echo from multiple speakers. An example is a smartphone that receives voice commands while the smartphone is playing music through stereo speakers. The system estimates residual echo level in different ways, and determines which estimate to use. The technique responds well to the difficult to handle scenario of a spatially quiescent image suddenly transitioning to a spatially rich image. Even in the face of such difficult scenarios, the system detects and removes residual echo from the microphone signal, instead of allowing the undesired residual echo to pass through.