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Redes neurais recorrentes lstm e suas aplicações
(Universidade Federal de Santa MariaBrasilUFSMCentro de Tecnologia, 2018-08-10)
The objective of this work is to review the Long Short-Term Memory (LSTM) artificial neural network model and its application in different activities, in order to evaluate its versatility. Four activities are presented, ...
Predicción de los precios del Bitcoin a partir de redes neuronales LSTM
(Universidad Católica de Pereira, 2023)
Automatic Translation of Spanish Natural Language Commands to Control Robot Comands Based on LSTM Neural Network
(2019)
In this paper, we propose a high level layer able to
translate motion commands in natural spanish language to a
formal intermediate representation called Robot Control
Language (RCL). The layer was built by using the ...
Prediction of imports of household appliances in Ecuador using LSTM networks
(Springer Nature Switzerland AG 2020, 2020)
Time series forecasting is an important topic widely addressed with traditional statistical models such as regression, and moving average. This work uses the state-of-the-art Long Short-Term Memory (LSTM) Networks to predict ...
Applied LSTM neural network time series to forecast household energy consumption
(SCOPUS, 2021-07)
In Ecuador, energy consumption is accentuated in
the residential sector due to population growth and other
parameters, which leads to an increase in energy costs,
greenhouse gas emissions and fossil fuel subsidies. ...
Improving post-filtering of artificial speech using pre-trained LSTM neural networks
(2019)
Several researchers have contemplated deep learning-based post-filters to increase the quality of statistical parametric speech synthesis, which perform a mapping of the synthetic speech to the natural speech, considering ...
Improving automatic speech recognition containing additive noise using deep denoising autoencoders of lstm networks
(2016)
Automatic speech recognition systems (ASR) suffer from performance degradation under noisy conditions. Recent work, using deep neural networks to denoise spectral input features for robust ASR, have proved to be successful. ...
Auto-Associative Initialization of LSTM Neural Networks for Fundamental Frequency Detection in Noisy Speech Signals
(2018)
In this paper, we present a new approach for fundamental frequency detection in noisy speech, based on Long Short-term Memory Neural Networks (LSTM). Fundamental frequency is one of the most important parameters of human ...