Neural Network Sound Localization via TDOA
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Solution Overview
Problem
Traditional methods for localizing acoustic signals in augmented reality environments are sensitive to environmental changes and often produce erroneous results due to variations in time-difference-of-arrival (TDOA) data, which is signal and noise rich and affected by interactions with physical objects.
Innovation Solution
Training an artificial neural network with both perturbed and non-perturbed TDOA data to robustly determine the spatial coordinates of acoustic signals, using a pre-determined microphone configuration and employing a local and cloud-based neural network setup for initial and precise calculations, respectively.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional methods are used to localize acoustic signals, then the system is simpler to implement, but the localization accuracy deteriorates due to sensitivity to environmental changes and noise in TDOA data
Solution Approach 1:
The neural network is trained in advance with perturbed TDOA data to learn robust localization patterns before actual use. This preliminary training phase enables the system to handle environmental variations and noise without increasing operational complexity
Solution Approach 2:
Traditional geometric or algorithmic localization methods are replaced with a neural network-based system. The neural network processes TDOA data differently, learning complex patterns that traditional methods miss, thereby improving accuracy despite the added computational complexity
2Reliability
If neural network training with perturbed data is implemented, then the robustness to TDOA data variations improves, but the training time and computational resources increase
Solution Approach 1:
The neural network is trained beforehand with various perturbed TDOA datasets to learn robust patterns. This preliminary training phase, while time-consuming, is performed offline so that the trained model can be deployed quickly for actual localization tasks without incurring training delays during operation
Solution Approach 2:
The training process uses various perturbation parameters (noise levels, environmental conditions) to create diverse training scenarios. By varying these parameters during training, the network learns to handle real-world variations efficiently, improving robustness without requiring excessive training time for each specific condition
Data Source
AI summary
The location of a sound within a given spatial volume may be used in applications such as augmented reality environments. An artificial neural network processes time-difference-of-arrival data (TDOA) from a known microphone array to determine a spatial location of the sound. The neural network may be located locally or available as a cloud service. The artificial neural network is trained with perturbed and non-perturbed TDOA data.