Echo Detection Using Matched Features from Microphone and Playout Audio
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Solution Overview
Problem
Acoustic and electrical echoes in audio systems negatively impact user experience in real-time communication, often misattributed to software issues when they are caused by hardware compatibility or quality, necessitating effective echo detection methods.
Innovation Solution
A method and system for echo detection involving data processing hardware that analyzes frequency representations of microphone and playout audio signals to identify matches and delay values indicative of echoes, using feature extraction, matching, and classification to determine echo presence, with optional echo reduction and feedback mechanisms.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If echo detection is implemented to identify hardware-related echo issues, then user experience is improved, but system complexity increases
Solution Approach 1:
The echo detection system segments the audio signal processing into distinct functional modules: frequency representation extraction, feature extraction, feature matching, and delay value analysis. Each module handles a specific aspect of echo detection, making the overall complex system manageable and maintainable while providing comprehensive echo identification capabilities
Solution Approach 2:
The system introduces intermediate processing steps between raw audio input and echo detection output, including frequency representation as an intermediary form and feature extraction as a mediator that transforms raw signals into comparable characteristics. These intermediaries simplify the detection process by breaking down the complex task of echo identification into manageable transformation stages
2Measurement precision
If frequency representation and feature matching are performed to detect echoes, then echo detection accuracy is improved, but processing time increases
Solution Approach 1:
The system extracts only the essential features from the frequency representations that are relevant for echo detection, rather than processing the entire frequency spectrum in detail. By taking out and focusing on specific matching features and delay values, the system achieves accurate echo detection while reducing unnecessary processing overhead
Solution Approach 2:
The feature matching process focuses on local characteristics of the frequency representations at specific time delays, rather than uniformly processing all frequency components and time periods. This localized approach to quality analysis improves detection accuracy for echo-specific patterns while minimizing processing of irrelevant audio characteristics
Data Source
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AI summary
A method (400) includes receiving a microphone audio signal (132) and a playout audio signal (112), and determining a frequency representation (324) of the microphone audio signal and a frequency representation of the playout audio signal. For each frequency representation, the method also includes determining features (302) based on the frequency representation. Each feature corresponds to a pair of frequencies (342) of the frequency representation and a period of time between the pair of frequencies. The method also includes determining that a match (212) occurs between a first feature based on the frequency representation of the microphone audio signal and a second feature based on the frequency representation of the playout audio signal, and determining that a delay value (222) between the first feature and the second feature corresponds to an echo within the microphone audio signal.