Passive Room Reverb Measurement From Natural Sound Decay
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Solution Overview
Problem
Current methods for measuring reverb characteristics in environments are expensive and not feasible with typical user computing devices, and existing approaches using loudspeakers and vision/depth sensors are inaccurate and time-consuming.
Innovation Solution
Determine reverb characteristics based on naturally occurring sound events passively recorded by user devices, using audio sensors to measure sound decay and background noise levels, and employ machine learning models to calculate reverb parameters.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional methods using loudspeakers and vision/depth sensors are used to measure reverb characteristics, then measurement capability is provided, but device complexity and cost increase significantly
Solution Approach 1:
The system uses the device's own audio sensor to capture naturally occurring sound events in the environment, eliminating the need for external loudspeakers or specialized measurement equipment. The device serves itself by utilizing its existing audio capabilities to perform reverb measurement.
Solution Approach 2:
The invention extracts only the audio signal processing functionality from the complex measurement system, using the audio sensor to capture sound events and then processing these signals through machine learning models to derive reverb characteristics without requiring additional hardware components.
2Measurement precision
If traditional reverb measurement methods are used, then reverb characteristics can be measured, but the process is time-consuming
Solution Approach 1:
The system continuously captures audio signals from naturally occurring sound events as users interact with the device in the environment, rather than requiring discrete measurement sessions. This continuous data collection enables ongoing reverb characteristic determination without interrupting user activities.
Solution Approach 2:
The machine learning model is pre-trained to recognize and process audio signals for reverb measurement, enabling rapid analysis of sound decay characteristics without requiring time-consuming manual measurement procedures during actual use.
3Measurement precision
If traditional reverb measurement approaches are used, then measurement data can be obtained, but accuracy is reduced due to environmental noise and interference
Solution Approach 1:
The system uses feedback from continuously monitored audio signals to distinguish between relevant sound decay information and background noise. The machine learning model processes audio feedback to identify patterns characteristic of reverb while filtering out environmental interference.
Solution Approach 2:
The system changes the parameter of analysis by transforming audio signals into frequency-domain representations and using machine learning models to extract reverb characteristics from modified signal parameters, enabling accurate measurement even in noisy environments where traditional time-domain methods fail.
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
Enables accurate and efficient measurement of reverb characteristics for rendering spatial audio, adapting to changes in environment acoustics, and providing an immersive soundscape without the need for additional equipment.
Implementation Method 1
receiving, from an audio sensor, an audio signal capturing an amount of sound emitted from a source within an environment over a period of time
Implementation Method 2
determining a reverb parameter measuring a decrease in the sound over the period of time
Data Source
AI summary
Disclosed implementations for determining a reverb characteristic of an environment. An audio signal capturing an amount of sound emitted from a source within an environment over a period of time is received from an audio sensor. A reverb parameter measuring a decrease in the sound over the period of time and a measure of the amount of sound that includes background noise in the environment are determined. The reverb characteristic of the environment is determined based on the reverb parameter and the measure. Spatial audio then rendered based on the reverb characteristic.