Multi-Device Acoustic Echo Cancellation With Sample Rate Offset Compensation
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Solution Overview
Problem
Multi-device scenarios in teleconferencing and human machine interaction suffer from acoustic echoes and sample rate offsets (SRO) that hinder effective interference cancellation and spatial audio reproduction due to unsynchronized device clocks, leading to degraded performance and impractical setups.
Innovation Solution
An apparatus and method for interference cancellation and sample rate offset compensation that includes a preprocessor for resampling audio signals, an interference estimator for estimating interference, and a signal processor for updating filter configurations to account for SRO, using multi-channel Kalman filters and dynamic weighted average coherence drift algorithms to synchronize and resample signals.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If synchronous solutions are used to estimate time scaling parameter and use time domain interpolation to synchronize signals before running AEC, then AEC performance is improved, but the solution is primarily limited to single-device scenarios and cannot handle multi-device scenarios with multiple independent clocks
Solution Approach 1:
The patent divides the multi-device AEC problem into separate single-device AEC problems by assigning each device its own reference signal and AEC processor. Each device independently estimates time scaling parameters and performs synchronization for its own reference signal, transforming a complex multi-device synchronization problem into multiple manageable single-device problems that can be solved in parallel.
Solution Approach 2:
The patent transitions from time-domain synchronization to frequency-domain synchronization by estimating time scaling parameters in the frequency domain using spectral correlation methods. This dimensional change allows the system to handle multi-device scenarios where time-domain approaches fail, as frequency-domain methods can accommodate independent device clocks and varying sampling rates.
2Adaptability or versatility
If asynchronous solutions with fixed beamformers are used to avoid explicit synchronization, then multi-device scenario compatibility is improved, but near-end speech distortion occurs when near-end speech leaks into beamformer output
Solution Approach 1:
The patent performs preliminary synchronization by estimating time scaling parameters and adjusting reference signals before they are used in the AEC process. This preliminary action in the frequency domain ensures that reference signals are properly synchronized with microphone signals, preventing near-end speech distortion while maintaining multi-device compatibility.
Solution Approach 2:
The patent replaces the mechanical/time-domain beamforming approach with a frequency-domain spectral correlation approach. Instead of using fixed beamformers that require precise time synchronization, the system uses frequency-domain spectral analysis to estimate time scaling parameters and synchronize signals, eliminating the need for complex beamforming hardware while maintaining speech quality.
3Device complexity
If sample rate offset compensation is not performed, then device complexity is reduced, but filter convergence is prevented and interference cancellation performance degrades
Solution Approach 1:
The patent implements self-service by having each device independently estimate its own time scaling parameter and perform its own reference signal synchronization. Each device's AEC processor automatically adapts to its own sampling rate characteristics without requiring external synchronization, enabling plug-and-play multi-device operation while maintaining filter convergence.
Solution Approach 2:
The patent dynamically changes the time scaling parameter based on estimated sample rate offsets. By continuously estimating time scaling parameters from spectral correlations and adjusting reference signal sampling rates accordingly, the system adapts to varying device characteristics and maintains optimal filter convergence conditions without fixed complexity overhead.
Data Source
AI summary
An apparatus for interference cancellation according to an embodiment is provided. The apparatus comprises a preprocessor configured for resampling a first audio signal to obtain a sampling-rate-adjusted first signal. Moreover, the apparatus comprises an interference estimator configured for estimating a first interference estimate depending on a first filter configuration and depending on the sampling-rate-adjusted first signal; and configured for estimating a second interference estimate depending on a second filter configuration and depending on a second audio signal. Furthermore, the apparatus comprises a signal processor configured for processing a microphone signal or an intermediate signal, being a signal derived from the microphone signal, depending on the first interference estimate and depending on the second interference estimate to obtain an error signal; configured for updating the first filter configuration and the second filter configuration depending on the error signal; and configured for outputting the error signal. The preprocessor is configured to resample the first audio signal depending on a sampling rate offset between a sampling rate of the microphone signal or of the intermediate signal or of the error signal, and a sampling rate of the first audio signal.