Adjoint-Based Identification of Sound Sources for Sound Reinforcement and Source Localization

The identification of sound sources is a common problem in acoustics. Different parameters are sought, among these are signal and position of the sources. We present an adjoint-based approach for sound source identification, which employs computational aeroacoustic techniques. Two different applications are presented as a proof-of-concept: optimization of a sound reinforcement setup and the localization of (moving) sound sources.

Supervised Learning for Multi Zone Sound Field Reproduction under Harsh Environmental Conditions

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.