Separate and Extract a Mixed Audio Using Deep Learning

The goal of this project was to use deep learning to extract and isolate a mixed audio file. A specific sound of interest nearly often overlaps with other waves from other sources, and if those waves are at a similar or greater amplitude, it will hinder the listener's ability to perceive the sound properly. We can concentrate on the specific sound of interest with the help of audio separation and extraction using deep neural network called CNN. The raised CNN autoencoder model has two convolution layers for the encoding stage, one fully connected layer for the next step, two deconvolution layers for each class, and an array of completely connected layers for the decoding stage.