Adaptive Echo Cancellation for Voice Recognition
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Solution Overview
Problem
Existing voice interaction systems face challenges in accurately recognizing voice commands due to background noise and acoustic echo, which can distort the audio signal and hinder the detection of user inputs, especially in environments with multiple noise sources and changing echo paths.
Innovation Solution
The implementation of a voice-controlled device that utilizes beamforming, adaptive echo cancellation, noise reduction, and residual echo suppression techniques, including adaptive filtering, to enhance the accuracy of automatic speech recognition by dynamically updating filter weights and applying them to subsequent filters to minimize echo and noise.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If adaptive filtering is used to reduce echo and noise, then speech recognition accuracy is improved, but device complexity increases
Solution Approach 1:
The audio signal processing is divided into multiple independent filter stages (first adaptive filter, second adaptive filter). Each filter handles specific aspects of echo and noise reduction separately, allowing for modular implementation and easier maintenance while achieving cumulative improvement in speech recognition accuracy
Solution Approach 2:
The first adaptive filter performs preliminary echo and noise reduction on the audio signal before it is processed by the second adaptive filter. This preliminary action removes the majority of interference, making the subsequent filtering more effective and reducing the overall complexity compared to attempting single-stage removal of all interference
2Measurement precision
If multiple adaptive filters are used to suppress residual echo and noise, then voice command detection accuracy is improved, but processing time increases
Solution Approach 1:
The adaptive filters are updated periodically based on incoming audio signals, with filter coefficients being adjusted at regular intervals rather than continuously. This periodic updating maintains detection accuracy while reducing computational overhead and processing time compared to continuous real-time adaptation
Solution Approach 2:
The system applies filtering beyond what a single filter could achieve by using multiple cascaded filters, but each individual filter is designed to handle only a portion of the total filtering task. This partial action approach distributes the processing load, preventing any single filter from becoming computationally overwhelming
Data Source
AI summary
The systems, devices, and processes described herein may generate a signal, such as an audio signal or an electrical signal, that may include echo and/or noise. The echo may be at least partially canceled based at least in part on acoustic echo cancellation or local echo cancellation, while the noise may be at least partially reduced based at least in part on noise reduction. A residual amount of echo and/or noise may then be determined. The residual echo and/or noise may be suppressed based at least in part on adaptive filtering of the signal, which may include modifying weights of an algorithm associated with an adaptive filter.