A conditioned autoencoder with position-dependent weights estimates acoustic transfer function magnitude distributions from sparse measurements, outperforming kernel ridge regression and neural field baselines in simulation.
Sound field recording using distributed microphones based on harmonic analysis of infinite order,
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
citation-role summary
background 1
citation-polarity summary
fields
cs.SD 1years
2025 1verdicts
CONDITIONAL 1roles
background 1polarities
support 1representative citing papers
citing papers explorer
-
Learning Magnitude Distribution of Sound Fields via Conditioned Autoencoder
A conditioned autoencoder with position-dependent weights estimates acoustic transfer function magnitude distributions from sparse measurements, outperforming kernel ridge regression and neural field baselines in simulation.