Orientation Tracking Prediction for Low-Latency Spatial Audio
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Solution Overview
Problem
Existing orientation tracking methods for audio processing in devices like smartphones and wireless earphones suffer from distracting delays due to non-negligible processing and transfer times, leading to inaccuracies and undesired audio artifacts, especially when predicting user orientation changes.
Innovation Solution
A method for predicting future orientation using a first-order predictor during acceleration and a second-order predictor during deceleration, with a computationally efficient approach that switches between these predictors based on angular velocity thresholds, ensuring accurate and timely audio rendering.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If current orientation tracking methods are used with standard processing times, then the system is simple to implement, but distracting delays and audio artifacts occur due to processing and transfer times of 200 milliseconds or more
Solution Approach 1:
The patent applies preliminary action by predicting future orientation before the actual audio rendering occurs. Instead of waiting for current orientation data to be processed (which causes 200ms+ delays), the system proactively estimates what the user's orientation will be at the future rendering time point, allowing audio to be prepared in advance with the correct spatial positioning, thereby eliminating distracting delays and artifacts.
2Measurement precision
If complex prediction algorithms are used to improve orientation accuracy, then prediction precision improves, but computational overhead increases making the system unsuitable for low-performance and battery-powered devices
Solution Approach 1:
The patent applies parameter changes by dynamically adjusting the prediction model complexity based on motion characteristics. The system analyzes angular velocity and acceleration parameters to determine the appropriate prediction approach: using simple linear extrapolation for steady-state motion (low computational cost) and more sophisticated models only when rapid orientation changes are detected (higher accuracy needed). This adaptive parameter adjustment maintains prediction precision while minimizing computational energy consumption on battery-powered devices.
Solution Approach 2:
The patent applies dynamics by making the prediction algorithm adaptive rather than static. The system continuously monitors motion parameters (angular velocity, acceleration) and dynamically selects or adjusts the prediction model complexity in real-time. This dynamic approach ensures high prediction accuracy during complex motions while using computationally efficient methods during stable periods, optimizing the balance between precision and energy consumption.
3Device complexity
If orientation data is transferred wirelessly between earphones and smartphone, then device complexity is reduced, but additional transfer time increases the overall delay beyond 200 milliseconds
Solution Approach 1:
The patent applies preliminary action by performing orientation prediction at the source device (earphones) before wireless transfer is needed. By estimating future orientation in advance and preparing the prediction results locally, the system reduces the amount of critical time-sensitive data that needs wireless transmission, and ensures that when data is transferred, it represents already-processed prediction information rather than raw sensor data requiring post-transfer processing.
Data Source
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AI summary
The present disclosure relates to a method and system for predicting a future orientation of an orientation tracker (100). The method comprising obtaining a sequence of angular velocity samples, each angular velocity sample indicating an angular velocity at a point in time and obtaining a sequence of angular acceleration samples, each angular acceleration sample indicating an acceleration or deceleration of the angular velocity at each point in time. Wherein said method further comprises determining (S5a), for each point in time where the angular velocity is accelerating, a predicted orientation of the orientation tracker (100) based on a first order prediction of an accumulated rotation of the orientation tracker (100) and determining (S5c), for each point in time where the angular velocity is decelerating, a predicted orientation of the orientation tracker (100) based on a second order prediction of the accumulated rotation of the orientation tracker (100).