Echo Cancellation via Environmental Change Detection
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Solution Overview
Problem
Existing echo cancellation technologies face difficulties in flexibly adapting to environmental changes, such as changes in room layout or noise levels, leading to suboptimal call quality and voice recognition performance.
Innovation Solution
A signal processing apparatus that includes an echo cancellation unit which learns the transfer characteristics of a space by reproducing sounds through a speaker and detecting environmental changes, allowing for adaptive echo cancellation across frequency bands.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If traditional echo cancellation with fixed filter coefficients is used, then initial convergence time is reduced, but the system cannot flexibly address environmental changes such as furniture placement
Solution Approach 1:
The echo cancellation system transitions from static filter coefficients to dynamic adaptation by detecting environmental changes and triggering re-learning of transfer characteristics. The system continuously monitors for environmental changes and updates echo cancellation parameters accordingly, making the system adaptable to changing acoustic conditions while maintaining fast initial convergence.
Solution Approach 2:
The system implements feedback by detecting environmental changes and using this information to trigger re-learning of transfer characteristics. The environmental change detection unit monitors the acoustic environment and provides feedback to the echo cancellation unit, which then updates its filter coefficients to maintain optimal performance despite environmental variations.
2Adaptability or versatility
If the system continuously learns transfer characteristics, then adaptability to environmental changes improves, but computational complexity and processing time increase
Solution Approach 1:
Instead of continuous learning, the system performs transfer characteristic learning periodically triggered by environmental change detections. The environmental change detection unit identifies when environmental changes occur and initiates re-learning only at these discrete moments, reducing unnecessary computational operations while maintaining adaptability to actual environmental variations.
Solution Approach 2:
The system performs preliminary environmental change detection before initiating full transfer characteristic re-learning. By first detecting environmental changes and then selectively triggering learning only when necessary, the system prepares in advance for potential adaptability needs while avoiding unnecessary computational complexity during stable environmental conditions.
Data Source
AI summary
It is desirable to provide an echo cancellation technique that enables an environmental change to be flexibly addressed. Provided is a signal processing apparatus including: an echo cancellation unit that learns an estimated transfer characteristic in a space through which a signal reproduced by a speaker is input to a microphone, and performs echo cancellation on the basis of the estimated transfer characteristic learned; and an environmental change detection unit that detects an environmental change, in which the echo cancellation unit learns the estimated transfer characteristic by causing the speaker to reproduce a sound for learning on the basis of detection of the environmental change.