Signal speech reconstruction and noise removal using convolutional denoising audioencoders with neural deep learning

• DOI: 10.1007/s10470-019-01446-6
• Publication Date: 2019-03-27T16:38:33Z
• Authors: Houda Abouzid; Otman Chakkor; Oscar Gabriel Reyes; Sebastian Ventura
• Keywords: Source Separation; SIGNAL (programming language); Signal reconstruction

Datasets exist in real life in many formats (audio, music, image,...). In our case, we have them from various sources mixed together. Our mixtures represent noisy audio data that need to be extracted (features), compressed and analysed in order to be presented in a standard way. The resulted data will be used for the Blind Source Separation task. In this paper, we deal with two types of autoencoders: convolutional and denoising. The novelty of our work is to reconstruct the audio signal in the output of the neural network after extracting the meaningful features that present the pure and the powerful information. Simulation results show a great performance, yielding of 87% for the reconstructed signals that will be included in the automated system used for real word applications.