
A conditional invertible neural network (CINN) is proposed to efficiently reconstruct sound fields in reverberant environments, balancing accuracy and computational efficiency while incorporating uncertainty estimates.


coremsg

efficient-sound-field-reconstruction-using-conditional-invertible-neural-networks


Efficient Sound Field Reconstruction using Conditional Invertible Neural Networks


title_rewrite


提案された客観的理解度尺度（OIM）であるGESIは、聴力損失音の実験を通じて正常聴力リスナーの音声理解度（SI）を予測することが可能であり、これにより補聴器向けのSEアルゴリズムの改善が可能となる。


gesi-gammachirp-envelope-similarity-index-for-predicting-speech-intelligibility-of-simulated-hearing-loss-sounds


GESI: Gammachirp Envelope Similarity Index for Predicting Speech Intelligibility of Simulated Hearing Loss Sounds



Utilizing RTF and LOCA for robust acoustic scene mapping in reverberant environments.


unsupervised-acoustic-scene-mapping-using-rtf-and-loca


Unsupervised Acoustic Scene Mapping Using RTF and LOCA
