Acoustic Echo Cancellation via Spectral Decomposition
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Solution Overview
Problem
Traditional echo cancellation techniques in audio communication devices are inadequate in dynamically adjusting to changing conditions and fail to effectively separate audio signals when both ends produce sound simultaneously, leading to persistent echo issues.
Innovation Solution
The method involves estimating the target audio signal by determining magnitude spectrograms for the mixture and received audio signals, using a magnitude spectral model to generate an output signal that suppresses echo, allowing for effective source separation in the magnitude spectral domain.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional linear transformation echo cancellation is used, then echo suppression is achieved, but the system cannot dynamically adapt to changing mixing conditions and environment
Solution Approach 1:
The patent applies dynamics by transitioning from static linear transformation models to dynamic spectral decomposition. The system continuously updates spectral estimates and adapts its cancellation filters based on real-time spectral characteristics of the echo and target signals, enabling dynamic response to changing acoustic environments while maintaining reliable echo suppression.
Solution Approach 2:
The patent changes parameters by operating in the spectral domain rather than the time domain. It transforms audio signals into spectral representations and adjusts cancellation parameters based on spectral characteristics, allowing the system to adapt to varying acoustic conditions more effectively while maintaining echo suppression performance.
2Reliability
If adaptive filters are used to model transformation effects, then echo cancellation is improved, but the filter cannot effectively adapt when both ends simultaneously produce sound
Solution Approach 1:
The patent segments the audio signal processing into distinct spectral components. By separating the echo signal and target signal into different spectral estimates, the system can independently process and cancel each component, enabling effective adaptation even when both ends produce sound simultaneously without interfering with each other's cancellation.
Solution Approach 2:
The patent introduces spectral decomposition as an intermediary between the mixed audio signal and the cancellation process. This spectral intermediary allows the system to identify and separate echo components from target components, enabling simultaneous adaptation to both sound sources without the filter becoming confused by overlapping time-domain signals.
3Quantity of substance
If the microphone detects both speaker audio and local source audio, then complete audio capture is achieved, but echo transmission to remote source occurs
Solution Approach 1:
The patent extracts the harmful echo component from the captured audio mixture using spectral decomposition. By identifying the spectral characteristics of the echo signal and separating it from the target signal, the system removes only the echo portion while preserving the local source audio, thus preventing echo transmission to the remote source without losing captured audio information.
Solution Approach 2:
The patent converts the harmful echo signal into a beneficial cancellation reference. By spectrally analyzing the echo component and using its spectral characteristics to guide the cancellation process, the system transforms the problematic echo into useful information for accurate echo suppression, effectively eliminating echo transmission while maintaining complete audio capture.
Data Source
AI summary
A method and apparatus for canceling an echo in audio communication is disclosed. The method comprises receiving an audio signal from a network and subsequently detecting a mixture audio signal comprising a target audio signal and an echo audio signal, the echo signal corresponding to the received audio signal. The method then comprises estimating the target audio signal by determining magnitude spectrograms for the mixture and received audio signals respectively, estimating a magnitude spectrogram of the target audio signal dependent on those of the mixture and received audio signal, and generating an output audio signal that estimates the target audio signal, the output audio signal being dependent on the estimated magnitude spectrogram.