Adaptive Echo Suppression Using Classification-Based Model Segmentation
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Solution Overview
Problem
Existing audio processing systems face challenges in accurately reducing noise and echo due to the dynamic nature of acoustic environments, leading to mistaken adaptation of speech and noise models, which results in residual echo and damaged speech in communication systems like teleconferencing.
Innovation Solution
A multi-faceted analysis is performed to derive an echo model, using echo classification information to build near-end speech and noise models, which prevents adaptation to acoustic echo, thereby applying signal modifications to reduce echo and noise components in the acoustic signal.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the noise reduction system continuously updates the transfer function to model the changing echo path, then echo cancellation performance is improved, but the system becomes more complex and less stable
Solution Approach 1:
The system performs preliminary actions by continuously monitoring and updating the transfer function before echo cancellation is needed. This proactive approach allows the system to adapt to changing acoustic environments in advance, maintaining echo cancellation performance without increasing operational complexity during actual communication.
Solution Approach 2:
The system implements feedback mechanisms where the transfer function is continuously updated based on residual echo analysis. This feedback loop allows the system to learn from previous cancellation attempts and adjust its model, improving reliability while keeping the update process automated and transparent.
2Reliability
If the noise reduction system adapts speech and noise models to improve noise reduction, then noise suppression performance is improved, but the system mistakenly adapts to echo resulting in damaged speech
Solution Approach 1:
The system segments the acoustic signal into distinct components: echo, near-end speech, and noise. By separating these components before model adaptation, the system can build accurate speech and noise models without being contaminated by echo, preventing the harmful effect of echo leakage while maintaining noise suppression performance.
Solution Approach 2:
The system introduces an intermediary echo cancellation stage that processes the signal before the noise reduction system adapts its models. This intermediary step removes or reduces echo components, acting as a mediator that protects the speech and noise models from being corrupted by echo, thereby preventing damaged speech output.
3Measurement precision
If the echo path is constantly updated to account for changing acoustic environment, then echo cancellation accuracy is improved, but the transfer function becomes less stable and more difficult to model
Solution Approach 1:
The system implements dynamics by allowing the transfer function to adapt continuously to changing acoustic environments. Rather than using a fixed model, the system dynamically updates the transfer function parameters in response to measured residual echo, achieving high prediction accuracy while accepting that stability is a moving target in non-stationary environments.
Solution Approach 2:
The system changes parameters of the transfer function model based on measured acoustic conditions. By adjusting model parameters dynamically rather than using fixed values, the system can maintain high echo prediction accuracy across varying acoustic environments, effectively managing the trade-off between accuracy and stability through adaptive parameter estimation.
Data Source
AI summary
The present technology provides adaptive noise and echo reduction of an acoustic signal which can overcome or substantially alleviate problems associated with mistaken adaptation of speech and noise models to acoustic echo. The present technology carries out a multi-faceted analysis to identify echo within the near-end acoustic signal to derive an echo model. Echo classification information regarding the derived echo model is then utilized to build near-end speech and noise models. These echo, speech, and noise models are then used to generate one or more signal modifications applied to the acoustic signal to preserve the desired near-end speech signal and reduce the echo and near-end noise signals. By building near-end speech and noise models utilizing echo classification information, the present technology can prevent adaptation of the speech and noise model to the acoustic echo.