Compressed Representation of Cepstral Coefficients via Recurrent Neural Networks for Informed Speech Enhancement

Abstract

Speech enhancement is one of the biggest challenges in hearing prosthetics. In face-to-face communication devices have to estimate the signal of interest, but playback of speech signals from an electronic device opens up new opportunities. Audio signals can be enriched with hidden data, which can subsequently be decoded by the receiver. We investigate a hybrid strategy made of signal processing and RNN (Recurrent Neural Networks) to calculate and compress cepstral coefficients: these are descriptors of the speech signal, which can be embedded in the signal itself and used at the receiver's end to perform an Informed Speech Enhancement. Objective evaluations showed an increase in speech quality for noisy signals enhanced with our method.

Time-Domain Anti-Interference Method for Ship Radiated Noise Signal

“…This paper will focus on signal enhancement methods based on data-driven deep learning methods. Due to the dependence of deep learning on massive data, current signal enhancement methods focus on the field of speech enhancement research [4][5][6][7][8][9][10][11][12][13]. Among them, the single-channel time domain signal enhancement method based on deep learning is to establish the mapping relationship between the enhanced signal and the mixture signal through the deep learning model, directly realize the signal enhancement in the time domain [14][15][16][17][18][19][20][21][22][23].…”

“…Due to the dependence of deep learning on massive data, current signal enhancement methods focus on the field of speech enhancement research [4][5][6][7][8][9][10][11][12][13]. Among them, the single-channel time domain signal enhancement method based on deep learning is to establish the mapping relationship between the enhanced signal and the mixture signal through the deep learning model, directly realize the signal enhancement in the time domain [14][15][16][17][18][19][20][21][22][23]. The method of using the time domain has certain advantages, avoiding the calculation of the mutual conversion of the time domain to the frequency domain.…”