Autoencoders in Deep Neural Network Architecture for Real Work Applications
| Autor: | Otman Chakkor, Houda Abouzid |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
Work (electrical)
business.industry Computer science Noise reduction 0202 electrical engineering electronic engineering information engineering Neural network architecture 020206 networking & telecommunications 020201 artificial intelligence & image processing 02 engineering and technology Artificial intelligence business |
| DOI: | 10.4018/978-1-7998-0117-7.ch007 |
| Popis: | The most heard sound exists as a mixture of several audio sources. All human beings have the ability to concentrate on a single source of their interest and ignore the other sources as disturbing background noise. To apply this powerful gift to a machine, it must obligatory pass through the source separation process. If there is not enough information about the process of mixture of those sources and their nature as well, the problem is known by Blind Source Separation BSS. This thesis is dedicated to study the BSS as a solution for human machine interaction. The objective consists in recovering one or several source signals from a given mixture signal. Recently, the science research is towards artificial intelligence and machine learning applications. The proposed approach for the separation will be to apply a Deep Neural Network method based on Keras. Extracting features from the audio with signal processing techniques and machine learning to learn a representation from the audio for the compression tasks and the suppression of the noise will improve the state-of-the-art. |
| Databáze: | OpenAIRE |
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