Head-Related Transfer Function Generation via Style Transfer
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Solution Overview
Problem
Existing techniques are ineffective for individualizing head-related transfer function measurements, leading to a lack of personalized audio signal generation for individuals, as public-domain databases often lack sufficient similarity to individual binaural sound characteristics.
Innovation Solution
A style transfer operation is used to combine content features of one audio signal with style features of another, generating an individualized head-related transfer function measurement pair by selecting a reference head-related transfer function from a database and applying a deep learning scheme, such as a convolutional neural network, to create a personalized audio signal.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If public-domain databases are used for head-related transfer function measurements, then general audio signal generation is possible, but individualization and personalization are insufficient
Solution Approach 1:
The patent uses style transfer to copy the style features from individual head-related transfer function measurements into the generated audio signals. The style transfer model learns from individual measurement data and replicates their unique acoustic characteristics, enabling high-fidelity personalization while maintaining the utility of general database resources.
Solution Approach 2:
The patent changes the parameters of head-related transfer function measurements by applying style transfer operations. This transforms generic measurements into individualized versions by adjusting stylistic parameters to match specific subject characteristics, thereby resolving the contradiction between general database availability and individualization needs.
2Adaptability or versatility
If style transfer operation is applied to combine content and style features, then individualized audio signals are generated, but computational complexity increases
Solution Approach 1:
The patent performs preliminary action by pre-training the style transfer model using a large dataset of head-related transfer function measurements from multiple subjects. This pre-training phase captures the essential style features and relationships, allowing the model to generate individualized audio signals efficiently during inference without requiring complex real-time computations for each new subject.
Solution Approach 2:
The style transfer model acts as an intermediary between the raw individual measurements and the final audio signal generation. It mediates the complexity by learning compact representations of individual characteristics during training, then applying these representations to generate personalized audio signals, thereby reducing the computational burden during actual use.
Data Source
AI summary
Methods, systems, and devices for head-related transfer function generation are described. A device may receive a digital representation of a first audio signal associated with a location relative to a subject, and select from a database a first reference head-related transfer function measurement pair corresponding to the location of the first audio signal. The device may then obtain a second head-related transfer function measurement pair by performing a style transfer operation on the selected reference head-related transfer function measurement pair based on a set of head-related transfer function measurement pairs specific to the subject. As a result, the device may output a second audio signal based on the digital representation of the first audio signal and the second head-related transfer function measurement pair.