2021/12/14 by Henry Sallandt, Sallandt, Henry, Philipp Krah +3
Computer Science · Earth and Planetary Sciences · Engineering · Physics and Astronomy · #Audio and Speech Processing (eess.AS) #FOS: Computer and information sciences #FOS: Electrical engineering #FOS: Physical sciences #Flow Measurement and Analysis #Fluid Dynamics (physics.flu-dyn) #Seismic Waves and Analysis #Sound (cs.SD) #Underwater Acoustics Research #cs.SD #eess.AS #electronic engineering #information engineering #physics.flu-dyn
paper · pdf · doi:10.48550/arxiv.2112.07349
Preprint submitted for publication
arxiv created 2021/12/14 · openalex publication_date 2021/12/14 · arxiv updated 2021/12/15 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
This manuscript presents an approach for multi zone sound field reproduction using supervised learning. Traditional multi zone sound field reproduction methods assume constant speed of sound, neglecting nonlinear effects like wind and temperature stratification. We show how to overcome these restrictions using supervised learning of transfer functions. The quality of the solution is measured by the acoustic contrast and the reproduction error. Our results show that for the chosen setup, even with relatively small wind speeds, the acoustic contrast and reproduction error can be improved by up to 16 dB, when wind is considered in the trained model.