Hybrid speech enhancement with wiener filters and deep LSTM denoising autoencoders
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| Autores: | , , , |
|---|---|
| Formato: | comunicación de congreso |
| Fecha de Publicación: | 2018 |
| Descripción: | 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-noise levels. Results show that this hybrid system improves the signal's enhancement significantly in comparison to the Wiener filtering and the LSTM networks separately. |
| País: | Kérwá |
| Institución: | Universidad de Costa Rica |
| Repositorio: | Kérwá |
| Lenguaje: | Inglés |
| OAI Identifier: | oai:kerwa.ucr.ac.cr:10669/86294 |
| Acceso en línea: | https://ieeexplore.ieee.org/abstract/document/8464132 https://hdl.handle.net/10669/86294 |
| Palabra clave: | Deep learning Denoising autoencoders Long short-term memory (LSTM) Signal processing |