Universidad de Costa Rica

Hybrid Speech Enhancement with Wiener filters and Deep LSTM Denoising Autoencoders


Colaboradores:
Ing. Marvin Coto Jiménez, PhD.
Autores:
Marvin Coto-Jimenez and John Goddard-Close and Leandro Di Persia and Hugo Leonardo Rufiner
Revista:
N/A
Editor:
IEEE
URL:
https://ieeexplore.ieee.org/abstract/document/8464132/

Resumen:

Over the past several decades, numerous speech enhancement techniques have been proposed to improve the performance of modern communication devices in noisy environments. Among them, there is a large range of classical algorithms (e.g. spectral subtraction, Wiener filtering and Bayesian-based enhancement), and more recently several deep neural network-based. In this paper, we propose a hybrid approach to speech enhancement which combines two stages: In the first stage, the well-known Wiener filter performs the task of enhancing noisy speech. In the second stage, a refinement is performed using a new multi-stream approach, which involves a collection of denoising autoencoders and auto-associative memories based on Long Short-term Memory (LSTM) networks. We carry out a comparative performance analysis using two objective measures, using artificial noise added at different signal-to …

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