Personalized HRTF Calibration for Virtual Surround Sound
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Solution Overview
Problem
Current virtual surround sound systems fail to provide personalized audio experiences due to the 'one size fits all' approach, as they do not account for individual head and ear shapes, leading to poor performance and lack of persistence across multiple devices.
Innovation Solution
A method and graphical user interface for calibrating head-related transfer functions (HRTFs) using user-input morphological parameters and virtual speaker positioning, allowing users to customize their audio experience and store settings in a cloud for synchronization across devices.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If a one size fits all approach is used for virtual surround sound systems, then device complexity is reduced, but audio localization accuracy deteriorates
Solution Approach 1:
The system applies individualized HRTF parameters specific to each user's head and ear morphology to the audio signal processing, rather than using a universal processing approach. This allows the audio localization to be optimized for each user's specific anatomical characteristics while maintaining a standardized system architecture.
Solution Approach 2:
The system dynamically adjusts HRTF parameters based on user-specific morphological data (head size, ear shape, etc.) to optimize audio localization accuracy for each individual user, transforming the fixed parameters of a one-size-fits-all system into adaptive parameters that match individual anatomical variations.
2Measurement precision
If user-specific HRTF calibration is implemented, then audio localization accuracy is improved, but device complexity increases
Solution Approach 1:
The system creates digital copies or models of user-specific HRTF parameters based on morphological measurements, storing these parameter sets for reuse across multiple devices and applications. This eliminates the need to perform complex calibration procedures on each device, reducing operational complexity while maintaining high localization accuracy.
Solution Approach 2:
The system designs a universal calibration framework that can be implemented across different audio reproduction systems and devices. The HRTF parameter sets are made device-agnostic, allowing the same calibrated parameters to be applied universally across multiple platforms, thereby reducing the need for device-specific customization.
3Stability of the object's composition
If HRTF parameters are stored locally on each device, then audio experience consistency is improved, but data management complexity increases
Solution Approach 1:
The system merges the data storage and synchronization functions into a unified cloud-based infrastructure, consolidating HRTF parameter management across multiple devices. This centralizes data management, reducing the complexity that would arise from managing separate local storage systems on each device while ensuring consistent audio experience across the device ecosystem.
Solution Approach 2:
The system introduces a cloud-based intermediary layer that mediates between multiple devices and the user's HRTF calibration data. This intermediary handles data synchronization, storage, and retrieval, eliminating the need for complex peer-to-peer data management between devices while ensuring consistent access to personalized audio parameters.
Data Source
AI summary
According to various embodiments, a method for outputting a modified audio signal may be provided. The method may include: receiving from a user an input indicating an angle; determining a parameter for a head-related transfer function based on the received input indicating the angle; modifying an audio signal in accordance with the head-related transfer function based on the determined parameter; and outputting the modified audio signal.