Surface Augmented Ray-Based Acoustic Modeling for Microphone Arrays
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Solution Overview
Problem
Conventional ray-based acoustic modeling is inadequate for microphone arrays mounted on rigid surfaces, as it fails to accurately account for plane wave scattering caused by the surface, leading to inaccuracies in modeling room acoustics and device responses.
Innovation Solution
The implementation of surface-augmented ray-based acoustic modeling, which combines ray tracing for room acoustics with a device acoustic dictionary to model scattering effects, using a superposition of plane wave responses to generate accurate microphone audio data and estimate room impulse response (RIR) data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional ray-based acoustic modeling is used, then computational complexity is reduced, but modeling precision deteriorates due to failure to account for plane wave scattering
Solution Approach 1:
The acoustic modeling problem is segmented into two independent parts: room acoustics modeling (handled by ray tracing) and device surface scattering modeling (handled by acoustic plane wave scattering). This segmentation allows each part to be modeled with appropriate simplicity while maintaining overall accuracy, resolving the contradiction between precision and complexity.
Solution Approach 2:
An intermediary approach is used where the device surface scattering is modeled as an additional component that interacts with the ray tracing results. The acoustic plane wave scattering model acts as a mediator between the simple ray tracing and the complex wave physics, providing corrections without requiring full wave-based modeling throughout.
2Productivity
If ray tracing is used for room acoustics, then computational complexity is reduced, but measurement precision deteriorates due to inability to model surface scattering effects
Solution Approach 1:
The acoustic field is segmented into room propagation components (modeled by ray tracing for efficiency) and surface scattering components (modeled by acoustic plane wave scattering for precision). This allows computational efficiency to be maintained while improving accuracy through the addition of scattering corrections.
Solution Approach 2:
The model transitions from purely ray-based parameters to a hybrid parameter set that includes both ray tracing results and acoustic plane wave scattering parameters. This parameter change enables the system to capture surface scattering effects while maintaining the computational efficiency of ray tracing.
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
This approach enhances the accuracy of acoustic modeling by accounting for surface scattering, improving audio processing tasks such as sound equalization, echo cancellation, and beamforming, while reducing computational complexity.
Implementation Method 1
The acoustic model is based on a combination of ray tracing and acoustic plane wave scattering
Implementation Method 2
plane wave scattering caused by the surface of the device
Data Source
AI summary
Disclosed are techniques for performing ray-based acoustic modeling that models scattering of acoustic waves by a surface of a device. The acoustic modeling uses two parameters, a room response representing acoustics and geometry of a room and a device response representing acoustics and geometry of the device. The room response is determined using ray-based acoustic modeling, such as ray tracing. The device response can be measured in an actual environment or simulated and represents an acoustic response of the device to individual acoustic plane waves. The device applies a superposition of the room response and the plane wave scattering from the device response to determine acoustic pressure values and generate microphone audio data. The device can estimate room impulse response (RIR) data using data from the microphones, and can use the RIR data to perform audio processing such as sound equalization, acoustic echo cancellation, audio beamforming, and/or the like.