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Robustness of LSTM neural networks for the enhancement of spectral parameters in noisy speech signals
(2019)
In this paper, we carry out a comparative performance analysis of Long Short-term Memory (LSTM) Neural Networks for the task of noise reduction. Recent work in this area has shown the advantages of this kind of network for ...
Assessing the robustness of recurrent neural networks to enhance the spectrum of reverberated speech
(2020)
Implementing voice recognition systems and voice analysis in real-life contexts present important challenges, especially when signal recording/registering conditions are adverse. One of the conditions that produce signal ...
Efficient Gated Convolutional Recurrent Neural Networks for Real-Time Speech Enhancement
Deep learning (DL) networks have grown into powerful alternatives for speech enhancement and have achieved excellent results by improving speech quality, intelligibility, and background noise suppression. Due to high ...
Regression-Based Noise Modeling for Speech Signal Processing
(2021-01-01)
Speech processing systems are very important in different applications involving speech and voice quality such as automatic speech recognition, forensic phonetics and speech enhancement, among others. In most of them, the ...
An immunological approach based on the negative selection algorithm for real noise classification in speech signals
(2017-02-01)
This paper presents a new approach to detect and classify background noise in speech sentences based on the negative selection algorithm and dual-tree complex wavelet transform. The energy of the complex wavelet coefficients ...
LSTM deep neural networks postfiltering for enhancing synthetic voices
(2018)
Recent developments in speech synthesis have produced systems capable of producing speech which closely resembles natural speech, and researchers now strive to create models that more accurately mimic human voices. One ...