Iterative Sound Source Localization Using TDOA and VDAA
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Solution Overview
Problem
Current sound source localization techniques are cumbersome and expensive due to the complexity of computing source location from time and volume differences measured by multiple microphones, especially in augmented reality environments.
Innovation Solution
A smart sound source locator system that uses an iterative approach with time-difference-of-arrival (TDOA) and volume-difference-at-arrival (VDAA) calculations, selectively choosing microphone combinations to improve localization accuracy while minimizing computational costs and noise errors, by employing distributed microphones and augmented reality functional nodes connected to cloud services.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional non-iterative localization formulas are used, then computation is simpler, but localization accuracy deteriorates in complex environments
Solution Approach 1:
The patent applies iterative refinement to dynamically improve localization accuracy. The system starts with an initial estimate from simple formulas and repeatedly refines it by incorporating additional measurements and correcting errors, transforming a static calculation into a dynamic optimization process that converges to higher precision results.
Solution Approach 2:
The iterative localization process uses feedback from residual errors to continuously improve accuracy. Each iteration calculates the difference between predicted and actual sensor measurements, then uses this feedback to adjust the estimated source location, progressively reducing errors and improving precision.
2Measurement precision
If more microphones are used, then localization accuracy is improved, but hardware cost and device complexity increase
Solution Approach 1:
The patent uses partial action by selecting and using only the necessary subset of microphones for each localization task. Rather than requiring all microphones to be active simultaneously, the system can achieve accurate localization with a minimal subset, reducing hardware requirements while maintaining precision.
Solution Approach 2:
The microphone array is segmented into multiple subsets that can be independently processed. The system divides the full set of microphones into different groups or pairs, allowing localization to be performed using only relevant segments for each specific source location, thereby reducing overall system complexity.
3Measurement precision
If iterative techniques are used, then localization accuracy is improved, but computational cost increases
Solution Approach 1:
The system performs preliminary actions by pre-calculating geometric relationships, sensor positions, and optimization parameters before actual localization occurs. This preprocessing reduces the computational burden during real-time operation, allowing iterative refinement to proceed more efficiently with lower energy consumption.
Solution Approach 2:
The patent changes parameters by adapting the iteration process based on signal quality, source distance, and environmental conditions. When conditions permit, fewer iterations are performed; when precision is critical or conditions are challenging, additional iterations are executed, optimizing energy usage based on actual needs rather than always maximizing computation.
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 sound source localization accuracy while reducing computational expenses and noise errors, allowing for precise source identification in complex environments with improved efficiency and accuracy.
Implementation Method 1
Measurable differences in the arrival times of source signals among the sensors are used to infer the location of the source. In a constant velocity medium, the time differences of arrival (TDOA) are proportional to differences in source-sensor range (RD).
Implementation Method 2
A smart sound source locator determines the location from which a sound originates according to attributes of the signal representing the sound or corresponding to the sound being generated at a plurality of distributed microphones.
Data Source
AI summary
A system efficiently selects at least one device from multiple devices based on received audio signals. In some instances, the system receives audio signals from devices that each comprise at least one microphone. A respective audio signal of the audio signals includes a representation of a sound originating from a location. The system then determines a device to be used to respond to the sound. In some instances, the system analyzes times in which the received audio signals that represent the sound are generated and/or volumes of the sound as represented by the received audio signals. The system can then select the device based on the analysis.