HRTF Timbre Synthesis via Latent Vector Space
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Solution Overview
Problem
Existing methods for HRTF synthesis and personalization focus primarily on localisation features, limiting the level of personalization achievable and restricting the quality of the audio experience. Additionally, storing a large number of HRTFs for user selection poses storage challenges.
Innovation Solution
A computer-implemented method using a deep learning model to learn a latent vector space that encodes timbre information independently of localisation information, allowing for the synthesis of a timbre component of an HRTF. This method involves training an autoencoder model with a training dataset of timbre features, where the model learns to reconstruct input timbre features while discarding measurement angle information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If HRTF synthesis focuses primarily on localisation features, then localisation accuracy is improved, but the level of personalisation and audio quality is limited
Solution Approach 1:
The HRTF is segmented into two independent components: localisation features (spectral notches, ITD, ILD) and timbre features (formants, spectral envelope). This segmentation allows each component to be processed and personalised independently, resolving the contradiction by enabling full personalisation without compromising localisation accuracy. The timbre component is extracted using formant analysis and synthesized separately from the localisation component.
Solution Approach 2:
The timbre component is extracted from the HRTF using formant analysis, separating it from the localisation features. This extraction enables the timbre information to be captured, stored, and applied independently, allowing for enhanced personalisation while maintaining the integrity of localisation cues. The extracted formant parameters are used to synthesise personalised timbre characteristics.
2Adaptability or versatility
If a large number of HRTFs are stored for user selection, then personalisation quality is improved, but storage requirements increase
Solution Approach 1:
The HRTF personalisation approach changes from storing complete impulse response functions to storing compact parametric representations. Specifically, formant parameters (center frequency, bandwidth, amplitude) and spectral envelope coefficients are stored instead of full HRTF data. This parameter-based representation dramatically reduces storage requirements while maintaining personalisation quality, as only a small set of parameters needs to be stored and applied during audio rendering.
Solution Approach 2:
Instead of storing multiple complete HRTF measurements for different users, the system creates parametric copies using formant synthesis. The extracted formant parameters serve as compact templates that can be rapidly instantiated and applied to synthesise personalised timbre characteristics on-demand, eliminating the need for extensive storage of full HRTF datasets.
3Adaptability or versatility
If HRTF synthesis includes full timbre information, then audio quality and immersion are improved, but the complexity of synthesis increases
Solution Approach 1:
Formant analysis serves as an intermediary method that bridges the gap between complex HRTF measurements and simple synthesis parameters. The formant parameters act as intermediaries that capture the essential timbre characteristics in a compact, computationally efficient form. This intermediary representation simplifies the synthesis process while preserving the rich timbre information needed for high-quality audio and immersive experience.
Data Source
Figure 1A~1B
Figure 1C~1D
Figure 1E
AI summary
A computer-implemented method of training a deep learning model for use in synthesis of a head-related transfer function, HRTF, is disclosed. The method comprises: providing a training dataset comprising a plurality of timbre features, each timbre feature comprising an HRTF measurement of a subject at a particular measurement angle, where the HRTF measurement has been processed to remove localisation perception features of the HRTF; training an autoencoder model, that is conditioned using the measurement angle, to encode the input timbre feature into a latent vector space and reconstruct the input timbre feature from the latent vector space, thereby learning a latent vector space that encodes timbre information independent of the measurement angle, such that the latent vector space is usable to synthesise a timbre component of an HRTF. The method allows for generating a personalised timbre component of an HRTF to provide better personalisation of an HRTF, thereby providing improved binaural audio.