AR Acoustic Simulation via ML Image Classification
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Solution Overview
Problem
Conventional techniques for augmented reality (AR) struggle to efficiently match virtual sound with equivalent real-world sound, leading to aural delays and increased processing requirements, which degrade the user experience and increase costs.
Innovation Solution
The use of machine learning (ML) techniques to classify images of real-world environments directly into acoustic presets, allowing for the prediction of acoustic properties such as reverberation characteristics, and subsequently creating an acoustic environment simulation that seamlessly blends virtual sound with the local environment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional multistep processes are used to match virtual sound with real-world sound, then acoustic accuracy is improved, but processing complexity and aural delays increase
Solution Approach 1:
The patent replaces complex mechanical signal processing systems with a machine learning-based system that uses neural networks to directly predict acoustic parameters from images. This substitution eliminates multiple processing steps while maintaining acoustic accuracy through data-driven models trained on acoustic field data.
Solution Approach 2:
The patent changes the approach from processing audio signals through complex filters and transforms to directly predicting acoustic parameters (reverb time, clarity, warmth) from visual data. This parameter transformation enables simpler processing while maintaining accuracy through the learned relationships between images and acoustic characteristics.
2Measurement precision
If conventional multistep processes are used to match virtual sound with real-world sound, then acoustic accuracy is improved, but processing time and aural delays increase
Solution Approach 1:
The patent performs preliminary action by pre-training machine learning models on large datasets of acoustic field measurements and corresponding images. This pre-computation enables the system to quickly predict acoustic parameters in real-time without performing complex calculations during actual audio processing, thus reducing aural delays.
Solution Approach 2:
The patent substitutes time-consuming conventional signal processing algorithms with optimized machine learning inference that can rapidly predict acoustic parameters from images, significantly reducing processing time and eliminating noticeable aural delays in the audio output.
3Measurement precision
If conventional multistep processes are used to match virtual sound with real-world sound, then acoustic accuracy is improved, but device cost increases
Solution Approach 1:
The patent replaces expensive hardware-based acoustic measurement and processing systems with software-based machine learning models that run on standard processors. This substitution maintains acoustic accuracy while reducing hardware requirements and device cost through efficient neural network implementations.
Solution Approach 2:
The patent changes the computational approach from heavy numerical simulations and complex signal processing to optimized machine learning inference that can be performed with lower computational power, thereby reducing device cost while maintaining acoustic parameter accuracy.
4Measurement precision
If conventional multistep processes are used to match virtual sound with real-world sound, then acoustic accuracy is improved, but processing requirements and cost increase
Solution Approach 1:
The patent substitutes energy-intensive conventional acoustic simulation and signal processing systems with optimized machine learning models that require significantly less computational energy to produce comparable acoustic accuracy, thereby reducing processing requirements and energy consumption.
Solution Approach 2:
The patent changes the computational parameters and algorithms from heavy numerical methods to efficient machine learning inference that consumes less energy while maintaining acoustic parameter prediction accuracy, thus reducing overall processing energy requirements.
Data Source
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AI summary
A method comprises: receiving an image of a real-world environment; using a machine learning classifier, classifying the image to produce classifications associated with acoustic presets for an acoustic environment simulation, the acoustic presets each including acoustic parameters that represent sound reverberation; and selecting an acoustic preset among the acoustic presets based on the classifications.