Hybrid speech enhancement with wiener filters and deep LSTM denoising autoencoders
comunicación de congreso
Fecha
2018Autor
Coto Jiménez, Marvin
Goddard Close, John
Di Persia, Leandro
Ruffiner, Hugo Leonardo
Metadatos
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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.
External link to the item
10.1109/IWOBI.2018.8464132Colecciones
- Ingeniería eléctrica [150]