Autoregressive Residual Echo Suppression in Vehicle Audio
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Solution Overview
Problem
In vehicle audio systems, linear echo cancellation is insufficient in reverberant environments due to the long tail of the room impulse response and computation limitations, leading to residual echo issues that affect the intelligibility of hands-free telecommunications.
Innovation Solution
An autoregressive-based residual echo suppression system in the Short Time Fourier Transform (STFT) domain uses an autoregressive model to estimate the power spectral density of residual echo signals, isolating the voice signal from microphone signals and suppressing reflections without distorting the desired signal, employing a higher-order autoregressive model and Wiener filter algorithms.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If linear echo cancellation is used, then computation is simple and fast, but residual echo remains in reverberant environments
Solution Approach 1:
The patent segments the echo cancellation task into two distinct stages: linear echo cancellation (LEC) for the direct path echo, and nonlinear residual echo suppressor (NES) for the reverberant tail. This segmentation allows each component to specialize - LEC handles the computationally intensive direct echo removal, while NES focuses on the softer reverberation tail, achieving better overall cancellation without proportionally increasing complexity
Solution Approach 2:
The patent introduces an intermediary Wiener filter between the LEC and NES components. This Wiener filter acts as a mediator that processes the output of LEC and feeds it to NES, optimizing the transition between linear and nonlinear processing stages. The Wiener filter adapts to minimize mean square error, effectively bridging the gap between the two cancellation approaches
2Reliability
If nonlinear residual echo suppressor is added, then echo levels are reduced, but system complexity increases
Solution Approach 1:
The patent implements dynamic adaptation in the NES component, where the autoregressive model parameters and Wiener filter coefficients are continuously updated based on incoming signal characteristics. This dynamic behavior allows the system to adapt to changing acoustic environments and speech patterns, maintaining effective echo suppression without requiring overly complex fixed-structure designs
Solution Approach 2:
The patent changes key parameters of the suppression system based on signal conditions - specifically, the autoregressive model order and Wiener filter smoothing factor are adjusted dynamically. This parameter adaptation allows the system to optimize performance for different reverberation conditions and speech levels, achieving high reliability without permanent structural complexity
3Measurement precision
If autoregressive model with many parameters is used, then power spectral density estimation is accurate, but computation increases
Solution Approach 1:
The patent applies partial action by using an autoregressive model with a moderate number of parameters (not the maximum possible) to estimate the power spectral density of the residual echo. This partial modeling approach captures the essential characteristics of the reverberant tail without requiring a full-order model, achieving sufficient accuracy while limiting computational energy consumption
Data Source
AI summary
The present application relates to a system and method for providing autoregressive based residual echo suppression in the STFT domain in a bidirectional vehicle communications system including receiving, from a communications processor, a speaker signal for coupling to a speaker, receiving, from a microphone, a microphone signal wherein the microphone signal includes a voice signal and a residual echo signal, transforming the signals to the STFT domain generating an estimated power spectral density of the residual echo signal in response to a prior power spectral density of a prior residual echo signal, isolating the voice signal from the microphone signal by multiplying the estimated residual echo gain generated using the estimated power spectral density of the residual echo signal with the microphone signal, transforming the signals back to the time domain and coupling the voice signal to the communications processor.