DeepAI AI Chat
Log In Sign Up

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

12/14/2021
by   Henry Sallandt, et al.
0

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.

READ FULL TEXT

page 6

page 8

page 9

page 12

page 13

02/14/2022

An Experimental Study of Wind Resistance and Power Consumption in MAVs with a Low-Speed Multi-Fan Wind System

This paper discusses a low-cost, open-source and open-hardware design an...
12/05/2022

Sound emergence as a predictor of short-term annoyance from wind turbine noise

While sound emergence is used in several countries to regulate wind ener...
10/11/2021

Wind-robust sound event detection and denoising for bioacoustics

Sound recordings are used in various ecological studies, including acous...
04/27/2023

Deep sound-field denoiser: optically-measured sound-field denoising using deep neural network

This paper proposes a deep sound-field denoiser, a deep neural network (...
05/02/2018

Generation of Infra sound to replicate a wind turbine

We have successfully produced infrasound, as a duplicate of that produce...
08/17/2021

DeepEigen: Learning-based Modal Sound Synthesis with Acoustic Transfer Maps

We present a novel learning-based approach to compute the eigenmodes and...