Image-Based Acoustic Modeling for Listener-Environment Audio
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Solution Overview
Problem
Existing video calling technologies struggle with providing immersive audio on the listener side, often plagued by issues such as poor sound quality, distortions, echoes, and muffled speech due to differences in acoustic properties between the listener's environment and the speaker's environment.
Innovation Solution
A computing system uses image data from the listener's environment to determine an audio response, applying a model that identifies spatial features and generates updated audio to simulate the speaker's sound as if it originated from the listener's environment, adjusting for acoustic properties like echo, reverberation, and diffusion.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If standard audio transmission is used in video calling, then communication simplicity is maintained, but audio quality deteriorates due to acoustic property differences between environments
Solution Approach 1:
The system performs preliminary analysis of the listener's environment using image data before audio transmission, determining acoustic properties (echo, reverberation, diffusion, absorption) in advance. This allows the system to pre-compute appropriate audio adjustments based on the detected environment, improving audio quality without adding complex real-time processing during communication.
Solution Approach 2:
The patent introduces an intermediary processing layer that analyzes environment images and generates audio response parameters. This intermediary system acts as a mediator between the original audio signal and the final output, applying acoustic adjustments based on environmental analysis. The intermediary includes components that detect spatial features from images and translate them into audio processing parameters, resolving the contradiction by adding intelligence without proportionally increasing complexity.
2Reliability
If environmental analysis is performed to improve audio immersion, then audio quality is enhanced, but processing time increases
Solution Approach 1:
The system performs environmental analysis and determines audio responses in advance, before actual audio playback. By analyzing the listener's environment images beforehand and pre-computing the appropriate acoustic adjustments, the system minimizes real-time processing delays during communication, thus improving audio immersion without significant processing time penalties.
Solution Approach 2:
The system applies selective audio adjustments based on the detected environment, focusing computational resources on the most impactful acoustic parameters. Rather than processing all possible audio parameters equally, the system identifies and adjusts only the critical acoustic properties (echo, reverberation, diffusion, absorption) that most significantly affect immersion, reducing overall processing time while maintaining immersion quality.
3Reliability
If acoustic adjustments are applied based on listener environment, then audio realism is improved, but computational resources are consumed
Solution Approach 1:
The system replaces complex real-time acoustic measurements and adjustments with an image-based environmental analysis approach. Instead of using microphones and sensors to physically measure the listener's environment in real-time, the system uses image data and computational models to infer acoustic properties. This substitution reduces the need for complex hardware and real-time computational resources while maintaining audio realism.
Solution Approach 2:
The system changes the parameters used for environmental analysis from physical acoustic measurements to visual image features. By detecting spatial features, objects, and materials from images rather than measuring acoustic properties directly, the system transforms the problem into a more computationally efficient domain. The model translates visual information into audio processing parameters, reducing computational energy requirements while achieving realistic audio output.
Data Source
AI summary
According to at least one implementation, a method includes receiving at least one image of a listener environment. The method further includes applying a model to the at least one image to determine an audio response for the listener environment. The method also includes generating updated audio based on audio received from a device and the audio response.