Adaptive Non-Linear Processor for Echo Suppression
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing echo cancellation technologies, particularly in two-way voice communications, face challenges in effectively removing residual echo due to variable acoustic echo paths and strong acoustic coupling between speakers and microphones, leading to inadequate suppression and distortion in speech.
Innovation Solution
An adaptive non-linear processor (NLP) algorithm that dynamically adjusts maximum attenuation based on echo detection results, utilizing multiple short-length adaptive filters across sub-bands to predict and cancel echo, ensuring robustness against various distortions and improving echo suppression.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a conservative setting for maximum NLP attenuation is used, then full-duplex behavior is maintained, but echo suppression is inadequate
Solution Approach 1:
The patent implements dynamic adjustment of NLP attenuation levels based on real-time echo detection. The system transitions from fixed conservative attenuation to adaptive attenuation that increases when echo is detected and decreases when it is not, resolving the contradiction between maintaining full-duplex behavior and suppressing residual echo.
Solution Approach 2:
The patent introduces a feedback mechanism where echo detection results from adaptive filters control the NLP attenuation level. The system continuously monitors for echo using multiple adaptive filters across sub-bands and adjusts attenuation accordingly, creating a closed-loop control that balances full-duplex operation with echo suppression.
2Object-affected harmful factors
If a higher-than-normal NLP attenuation is applied, then echo suppression is improved, but speech from local talker is distorted
Solution Approach 1:
The system uses feedback from echo detection to control attenuation levels. High attenuation is applied only when echo is detected by the adaptive filters, and normal attenuation is used when no echo is present, preventing unnecessary distortion of local talker speech while effectively suppressing echo when needed.
Solution Approach 2:
The patent dynamically changes the attenuation parameter based on echo detection results. The system adjusts the NLP attenuation level from conservative to higher-than-normal values only when echo is detected, optimizing the balance between echo suppression and speech quality preservation through parameter adaptation.
3Adaptability or versatility
If fixed maximum attenuation is used to satisfy broad categories, then device compatibility is maintained, but specific cases with strong acoustic coupling are not adequately suppressed
Solution Approach 1:
The patent replaces fixed attenuation settings with dynamic adaptive attenuation controlled by echo detection. The system automatically adjusts attenuation levels based on real-time conditions, providing both broad compatibility through adaptive behavior and targeted suppression for specific cases with strong acoustic coupling.
Solution Approach 2:
The system performs self-adjustment of attenuation levels based on its own echo detection capabilities. The adaptive filters continuously monitor for echo and the NLP automatically adjusts its attenuation to appropriate levels, eliminating the need for manual configuration or fixed settings while maintaining broad device compatibility.
Data Source
AI summary
Architecture that mitigates echo in voice communications using echo detection and adaptive management of attenuation by a non-linear processor (NLP). Suppression values provided by the NLP are determined based on echo detection and retained on a case-by-case basis to automatically increase or decrease the attenuation as needed. Feedback is incorporated that where the controls for the NLP attenuation is given by the amount of echo that remains, and this in turn affects the amount of echo that remains.