# LSTM based AE-DNN constraint for better late reverb suppression in multi-channel LP formulation

## TL;DR

This paper introduces an LSTM-based autoencoder to improve the estimation of the desired speech signal's PSD, enhancing late reverberation suppression in multi-channel linear prediction methods.

## Contribution

It proposes a novel LSTM-based autoencoder for PSD estimation, outperforming traditional methods in late reverberation suppression in reverberant speech.

## Key findings

- LSTM autoencoder outperforms traditional PSD estimation methods.
- Recurrent LSTM architecture yields better reverberation suppression.
- Improved speech quality in reverberant environments.

## Abstract

Prediction of late reverberation component using multi-channel linear prediction (MCLP) in short-time Fourier transform (STFT) domain is an effective means to enhance reverberant speech. Traditionally, a speech power spectral density (PSD) weighted prediction error (WPE) minimization approach is used to estimate the prediction filters. The method is sensitive to the estimate of the desired signal PSD. In this paper, we propose a deep neural network (DNN) based non-linear estimate for the desired signal PSD. An auto encoder trained on clean speech STFT coefficients is used as the desired signal prior. We explore two different architectures based on (i) fully-connected (FC) feed-forward, and (ii) recurrent long short-term memory (LSTM) layers. Experiments using real room impulse responses show that the LSTM-DNN based PSD estimate performs better than the traditional methods for late reverb suppression.