Adaptive Acoustic Echo Cancellation for Motile Devices
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Solution Overview
Problem
Acoustic echo cancellation in mobile devices is compromised by motion-induced changes in the audio channel and Doppler shifts, leading to less-than-optimal removal of far-end audio signals, which affects speech recognition and processing systems.
Innovation Solution
An autonomously motile device employs an adaptive filter with updating coefficients to model the changing audio channel, using algorithms like LMS or RLMS to subtract Doppler-shifted far-end audio from near-end audio, thereby isolating user audio effectively.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If acoustic echo cancellation is performed in a mobile device, then far-end audio can be isolated from near-end audio, but motion-induced changes in the audio channel and Doppler shifts cause cancellation errors
Solution Approach 1:
The patent implements dynamic adaptation of the acoustic echo cancellation system by continuously updating the adaptive filter coefficients based on detected motion parameters. The system transitions from a static cancellation model to a dynamic one that adjusts in real-time according to device motion, thereby maintaining cancellation accuracy despite changing acoustic channels and Doppler effects.
Solution Approach 2:
The system changes the parameters of the adaptive filter based on motion detection. When motion is detected, the system modifies filter coefficients, step-size parameters, and convergence criteria to compensate for Doppler shifts and channel variations. This parameter adaptation allows the system to maintain optimal performance under varying motion conditions.
2Productivity
If the device moves at high speed, then productivity is improved, but Doppler shifts increase causing worse acoustic echo cancellation
Solution Approach 1:
The system employs feedback mechanisms where motion sensors continuously monitor device movement and provide this information to the acoustic echo cancellation algorithm. The adaptive filter uses this feedback to adjust its coefficients in real-time, compensating for Doppler shifts caused by high-speed movement. This closed-loop feedback ensures that cancellation performance remains reliable even at high speeds.
Solution Approach 2:
The system performs preliminary detection of motion parameters before they affect the acoustic channel significantly. By anticipating motion-induced Doppler shifts and pre-adjusting the adaptive filter coefficients, the system maintains cancellation effectiveness during high-speed operation without waiting for degradation to occur.
3Measurement precision
If the adaptive filter coefficients are updated frequently to track channel changes, then cancellation accuracy improves, but computational complexity increases
Solution Approach 1:
The system dynamically adjusts the update frequency and step-size parameters of the adaptive filter based on detected motion characteristics. During high-motion periods, the system increases update frequency to track rapid channel changes. During low-motion periods, it reduces update frequency to minimize computational load. This adaptive parameter adjustment balances accuracy and complexity.
Solution Approach 2:
The adaptive filter implementation is made dynamic by adjusting its operational characteristics based on real-time motion detection. The system modulates the complexity of coefficient updates according to actual channel variation rates, using simpler update rules when motion is minimal and more complex updates when motion is detected, thereby optimizing the trade-off between accuracy and computational burden.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
The solution effectively reduces errors in acoustic echo cancellation due to motion and Doppler shifts, enhancing the accuracy of speech recognition and processing systems by isolating user audio amidst changing environments.
Implementation Method 1
The adaptive filter may be used to process the audio data to account for Doppler shifts. The Doppler shift may be determined, in whole or in part, based on a velocity of the autonomously motile device.
Data Source
AI summary
A device capable of motion includes an acoustic echo canceller for cancelling a reference signal from received audio data. The device updates an adaptive filter as the device moves to reflect the changing audio channel between a loudspeaker and a microphone of the device. A step size for changing coefficients of the filter is determined based on its velocity. A number of iterations for updating the filter using a frame of audio data is also determined based on the velocity.