2021 IEEE International Conference on Acoustics, Speech and Signal Processing

6-11 June 2021 • Toronto, Ontario, Canada

Extracting Knowledge from Information

2021 IEEE International Conference on Acoustics, Speech and Signal Processing

6-11 June 2021 • Toronto, Ontario, Canada

Extracting Knowledge from Information
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Paper Detail

Paper IDAUD-16.4
Paper Title PERSONALIZED HRTF MODELING USING DNN-AUGMENTED BEM
Authors Mengfan Zhang, Stanford University, United States; Jui-Hsien Wang, Adobe Research, United States; Doug James, Stanford University, United States
SessionAUD-16: Modeling, Analysis and Synthesis of Acoustic Environments 2: Spatial Audio
LocationGather.Town
Session Time:Wednesday, 09 June, 16:30 - 17:15
Presentation Time:Wednesday, 09 June, 16:30 - 17:15
Presentation Poster
Topic Audio and Acoustic Signal Processing: [AUD-SARR] Spatial Audio Recording and Reproduction
IEEE Xplore Open Preview  Click here to view in IEEE Xplore
Abstract Accurate modeling of personalized head-related transfer functions (HRTFs) is difficult but critical for applications requiring spatial audio. However, this remains challenging as experimental measurements require specialized equipment, numerical simulations require accurate head geometries and robust solvers, and data-driven methods are hungry for data. In this paper, we propose a new deep learning method that combines measurements and numerical simulations to take the best of three worlds. By learning the residual difference and establishing a high quality spatial basis, our method achieves consistently 2 dB to 2.5 dB lower spectral distortion (SD) compared to the state-of-the-art methods.