Spatial Audio Adjustment Using Head Rotation Prediction
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Solution Overview
Problem
Current spatial audio effects encounter significant transmission delays due to head rotation, which can exceed 204 milliseconds, affecting the quality of three-dimensional sound field experiences.
Innovation Solution
An adjustment method and computing apparatus that utilize a prediction model trained through machine learning to anticipate head rotation angles, adjusting audio characteristics such as amplitude and phase to reduce output delays by predicting future head positions and aligning audio signal adjustments accordingly.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If spatial audio effects are processed in real-time without prediction, then the system complexity remains low, but transmission delay increases to 204 milliseconds or more
Solution Approach 1:
The patent applies preliminary action by using a prediction model to forecast future head rotation angles before they actually occur. The system measures current attitude data, compares it with previously predicted data to determine evaluation data, generates future predicted data through the prediction model, and adjusts audio characteristics in advance based on these predictions. This proactive approach reduces transmission delay by anticipating user head movements rather than reacting to them after they occur.
2Measurement precision
If the prediction model uses more time intervals of attitude data, then the prediction accuracy improves, but the computational load increases
Solution Approach 1:
The patent implements feedback by comparing the current measured attitude data with previously predicted data to calculate an angle error. This feedback mechanism determines whether to select attitude data from more or fewer time intervals for evaluation. When the angle error is small (high prediction accuracy), the system can use fewer time intervals, reducing computational load. When the angle error is large (low prediction accuracy), the system selects data from more time intervals to improve prediction accuracy, thus dynamically adjusting computational resources based on actual performance needs.
Data Source
AI summary
An adjustment method of an audio signal and a computing apparatus for audio signal adjustment are disclosed. Current attitude data of a current time interval is measured. Data to be evaluated is determined based on an angle error between the current attitude data and previously predicted data. By inputting the data to be evaluated into a prediction model, future predicted data of a future time interval is generated. Audio characteristics of an audio signal are adjusted to a predicted rotation angle corresponding to the future time interval. Therefore, the listening experience can be improved.