ML Echo Reference Control for Complex Playback Echo Cancellation
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Solution Overview
Problem
Existing systems struggle to effectively eliminate echo in media playback environments due to unknown echo paths and additional digital signal processing operations performed by media playback devices, which traditional echo cancellation methods cannot account for.
Innovation Solution
A system using a machine learning model to generate echo reference microphone signals based on both microphone and input audio signals, allowing for improved echo cancellation by accounting for the echo path and additional signal processing operations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional echo cancellation methods are used, then the system is simple to implement, but echo cannot be effectively eliminated due to unknown echo paths and additional digital signal processing operations
Solution Approach 1:
The patent introduces an intermediary machine learning model that acts as a mediator between the input audio signal and the echo cancellation process. This ML model generates predicted echo signals that serve as a bridge, allowing the system to handle unknown echo paths and complex digital signal processing operations without requiring direct modeling of the entire echo path.
Solution Approach 2:
The patent replaces traditional mechanical/mathematical echo cancellation systems with a machine learning-based approach. Instead of using conventional signal processing algorithms that rely on known echo paths, the system uses trained neural networks to predict and cancel echo, substituting deterministic mechanical methods with probabilistic learning-based methods.
2Reliability
If machine learning models are used to account for complex signal processing, then echo cancellation effectiveness improves, but computational requirements and processing time increase
Solution Approach 1:
The patent applies preliminary action by training the machine learning models in advance during an offline phase. The ML models are pre-trained on large datasets to learn echo patterns and characteristics before deployment. This allows the system to perform rapid inference during actual echo cancellation without performing complex training computations in real-time, thus reducing processing time during operation.
3Measurement precision
If machine learning models are used to generate echo reference signals, then echo cancellation accuracy improves, but computational resources and system complexity increase
Solution Approach 1:
The patent segments the echo cancellation system into multiple specialized machine learning models, each handling specific aspects of echo prediction and cancellation. Instead of using one large complex model, the system divides functionality into separate ML components that can be independently trained and optimized, reducing the computational burden on any single component while maintaining overall accuracy.
Data Source
AI summary
A method performed by a media source device that has several microphones, the method includes receiving an input audio signal, providing the input audio signal to a media playback device that is communicatively coupled to a speaker, receiving, from the microphones, microphone signals that include the ambient sounds within an ambient environment in which the media source device is located and at least one sound of the input audio signal produced by the speaker, producing, using a machine learning (ML) model that has input based on the microphone signals and the input audio signal, an echo reference microphone signal that includes the at least one sound produced by the speaker, and producing an echo cancelled output audio signal by performing echo cancellation upon each of the microphone signals using the input audio signal and the echo reference microphone signal as reference signals.