Acoustic Device Using 2D Image Data for Spatial Filter Learning
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Solution Overview
Problem
Existing acoustic systems require complex 3D modeling and significant computational resources to accurately reproduce sound fields, especially in indoor spaces with varying reverberation characteristics, making it difficult to efficiently recreate sound fields from 2D image data in unknown spaces.
Innovation Solution
An acoustic device comprising an imaging device, a sound collector, and a computation part that uses deep learning to estimate and learn the parameters of a spatial acoustic filter from 2D image data, allowing for the reconstruction of sound field models and filter characteristics, thereby enabling stereophonic reproduction of sound fields in unknown spaces.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If 3D space model and ray acoustic modeling are used to calculate sound reflection, then acoustic characteristics can be accurately reproduced, but the computational load and amount of information required increase significantly
Solution Approach 1:
The patent uses 2D images as simplified copies of 3D space models to represent acoustic environments. Instead of performing complex 3D ray acoustic modeling, the system extracts acoustic parameters directly from 2D image data, maintaining sufficient accuracy for acoustic reproduction while dramatically reducing computational complexity. The 2D image serves as a surrogate that captures essential spatial information without requiring full 3D reconstruction.
Solution Approach 2:
The patent extracts only the necessary acoustic parameters (such as reverberation time, early decay time, and spatial impulse response characteristics) directly from 2D image data, rather than computing the complete 3D sound field model. This selective extraction approach obtains the essential acoustic characteristics needed for reproduction while avoiding the computational burden of calculating all 3D spatial parameters.
2Loss of information
If 3D space model is constructed from 2D image, then complete spatial information is obtained, but the processing time and computational resources increase
Solution Approach 1:
The system uses the 2D image itself as a sufficient representation of spatial information rather than converting it to a 3D model. The 2D image contains adequate spatial cues (perspective, scale, room geometry visualizations) that can be directly processed to extract acoustic parameters, eliminating the time-consuming 3D reconstruction step while retaining necessary spatial information.
Solution Approach 2:
The patent performs preliminary extraction of acoustic parameters directly from the 2D image before any 3D model construction would be attempted. By identifying and extracting key acoustic characteristics (reverberation characteristics, spatial impulse response) directly from 2D image features in advance, the system avoids subsequent time-consuming 3D processing steps.
Data Source
AI summary
An acoustic device includes: an imaging device configured to take a sample image of a space as a sound field and create an image data on the space based on the taken sample image; a sound collector configured to collect a sound generated in the space or to collect a previously-collected acoustic data therein; and a computation part configured to previously compute a plurality of parameters relevant to a coefficient of spatial acoustic filter corresponding to the sample image of the space and previously learn a sound field model of the space shown in the sample image. The computation part is configured to construct a sound field model of the sample image taken by the imaging device or of a previously-taken sample image, from the acoustic data collected by the sound collector, using the coefficient of spatial acoustic filter.