Personalized Binaural Audio Conversion Using User-Specific AI Models
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Solution Overview
Problem
Existing audio recording systems using dummy head microphones fail to account for individual user head and ear size variations, leading to inconsistent audio experiences and suboptimal conversion by artificial intelligence models.
Innovation Solution
An electronic apparatus utilizing an artificial intelligence model that learns from both general and binaural audio data, incorporating user-specific information to adjust audio output based on head and ear size, and potentially other contextual factors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If a fixed-size dummy head microphone is used for binaural recording, then the recording device structure is simple and manufacturing is easy, but the audio quality does not match individual user head and ear size variations
Solution Approach 1:
The patent applies parameter changes by training multiple AI models with different parameters corresponding to different head and ear size categories. Instead of physically changing the dummy head size, the system changes the computational parameters of the AI model to match individual user characteristics, thereby resolving the contradiction between fixed manufacturing and variable audio quality requirements
Solution Approach 2:
The patent uses AI models to create virtual copies of binaural audio effects that would otherwise require physical dummy heads of different sizes. By copying the acoustic characteristics through software models trained on binaural data, the system achieves personalized audio quality without manufacturing multiple physical devices
2Device complexity
If an AI model is trained on binaural audio data from a fixed dummy head size, then the model training process is simplified, but the converted audio data does not suit individual users with different head and ear sizes
Solution Approach 1:
The patent segments the user population into multiple categories based on head and ear size characteristics. Instead of creating one complex model for all users, the system divides the problem into multiple simpler models, each trained on binaural audio data corresponding to specific size categories, thereby balancing training simplicity with conversion suitability
Solution Approach 2:
The patent introduces dynamics by making the AI model selection adaptive rather than static. The system dynamically selects the appropriate pre-trained model based on the user's measured head and ear size, allowing the system to adapt to individual users without requiring complex retraining for each user
3Manufacturing precision
If multiple AI models are trained for different user characteristics, then audio conversion suitability for individual users is improved, but the system complexity and processing time increase
Solution Approach 1:
The patent applies preliminary action by pre-training multiple AI models offline for different user categories before deployment. The measurements of user head and ear sizes are taken in advance to select the appropriate pre-trained model, avoiding the need for real-time complex processing and reducing system runtime complexity
Solution Approach 2:
The patent uses dynamics by implementing a dynamic model selection mechanism that adapts to individual users based on their physical characteristics. The system measures user-specific parameters and dynamically selects the most suitable pre-trained model, achieving personalized audio quality without maintaining all models active simultaneously
4Productivity
If binaural audio data is recorded with a fixed dummy head, then the recording process is simple and fast, but the audio experience varies significantly across different users
Solution Approach 1:
The patent applies parameter changes by using pre-trained AI models with different parameters for different user categories. The system quickly determines the user's head and ear size parameters and selects the corresponding model, maintaining fast processing while adapting to individual user characteristics for personalized audio experience
Data Source
AI summary
An electronic apparatus is provided. The electronic apparatus includes a camera, a processor and a memory configured to store at least one instruction executable by the processor where the processor is configured to input input audio data to an artificial intelligence model corresponding to user information, and obtain output audio data from the artificial intelligence model, and the artificial intelligence model is a model learned based on first learning audio data obtained by recording a sound source with a first recording device, second learning audio data obtained by recording the sound source with a second recording device, and information on a recording device for obtaining the second learning audio data, and the second learning audio data is binaural audio data.