Estimating direct-to-reverberant ratio mapped from power spectral density using deep neural network

Yusuke Hioka; Kenta Niwa

ICASSP 2016accepted0 citations

Estimating direct-to-reverberant ratio mapped from power spectral density using deep neural network

Abstract

A new attempt for estimating the direct-to-reverberant ratio (DRR) by mapping the power spectral density (PSD) of the direct sound and reverberation using the deep neural network is reported. The method finds the correct DRR from the PSD estimated with an algorithm using a microphone array. The experimental results using a recording of a reverberant speech signal, which included various environmental noise, reveal that the proposed method is effective in improving the accuracy of DRR estimation and robust against various noise.

BibTeX

@inproceedings{icassp2016_estimatingdirect,
  title = {Estimating direct-to-reverberant ratio mapped from power spectral density using deep neural network},
  author = {Yusuke Hioka and Kenta Niwa},
  booktitle = {ICASSP 2016},
  year = {2016}
}