Room Impulse Response Estimation for Acoustic Reflection Detection
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Solution Overview
Problem
Electronic devices face performance degradation due to the presence of acoustically reflective surfaces, which affect speech recognition and sound quality by altering the acoustic system's transfer function, leading to challenges in echo cancellation and beamforming.
Innovation Solution
A system that uses a microphone array and loudspeakers to determine the aggregate impulse response, perform deconvolution to estimate the room impulse response, and detect peaks to determine distances and directions of acoustically reflective surfaces, employing sparse deconvolution algorithms and beamforming techniques to improve echo cancellation and sound quality.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional echo cancellation and beamforming methods are used, then device complexity is reduced, but speech recognition accuracy and sound quality deteriorate in environments with acoustically reflective surfaces
Solution Approach 1:
The system performs preliminary wall detection and room impulse response estimation before speech recognition and beamforming operations. By pre-characterizing the acoustic environment (detecting reflective surfaces and estimating RIR), the system prepares compensation parameters in advance, allowing standard speech recognition and beamforming algorithms to operate more effectively without requiring complex real-time adaptations.
Solution Approach 2:
The patent introduces wall detection and room impulse response estimation as intermediary processes between the microphone array and the speech recognition system. These intermediaries characterize the acoustic environment and provide correction information, acting as a bridge that enables standard algorithms to perform better in reflective environments without directly modifying their core complexity.
2Measurement precision
If acoustically reflective surfaces are present, then echo cancellation becomes more difficult, but speech recognition performance deteriorates
Solution Approach 1:
The system uses detected wall positions and estimated room impulse responses to create feedback information that compensates for acoustic reflections. By continuously characterizing the acoustic environment and using this information to adjust processing, the system improves echo cancellation accuracy and maintains speech recognition performance in reflective conditions.
3Reliability
If beamforming techniques are applied, then sound quality improves, but device complexity increases
Solution Approach 1:
The system performs preliminary wall detection and acoustic environment characterization before beamforming operations. By pre-identifying reflective surfaces and estimating room impulse responses, the system prepares environment-specific parameters that simplify the beamforming process, allowing standard beamforming algorithms to achieve better sound quality without requiring overly complex real-time processing.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
The system effectively mitigates the negative impact of acoustically reflective surfaces by accurately estimating room impulse responses, enhancing speech recognition and sound quality through improved echo cancellation and beamforming, even in environments with multiple reflective surfaces.
Implementation Method 1
A loudspeaker of the microphone array generates audible sound(s) based on playback audio data
Implementation Method 2
the microphone array detects reflected sound waves reflected by the acoustically reflective surface
Data Source
AI summary
A system that performs wall detection, range estimation, corner detection and/or angular estimation. The system may determine an aggregate impulse response (e.g., impulse response of all components in a room) and may perform a deconvolution to remove a system impulse response (e.g., impulse response associated with loudspeaker(s) and microphone(s)). Thus, the system may use a sparse deconvolution algorithm to estimate a room impulse response (e.g., determine acoustic characteristics of the room). The system may detect a peak in the room impulse response and determine a distance and/or direction to an acoustically reflective surface based on the peak.