Singing voice separation based on U-NET neural network and its application

Abstract

Singing Voice separation is a very important method that helps people to obtain a vocal or accompaniment for further usage like music composing. In this paper, I developed a Song accompaniment separation application based on Convolutional neural network Unet model and Deep learning (DL). Basically, the U-net convolutional network is applied to the two-dimensional spectrum of a music audio in order to achieve a more precise separation effect. During this deep learning process, a significant amount of data was inserted into the model to get the best fit parameters. Then, after all, the model can automatically generate a mask that filters out the accompaniment or vocal part that users want.. This completed model is then transmitted to the terminal of a internet server to enable users to get access to the model through internet. After the test of both quantitative evaluation and subjective assessment, the application impressively achieved the Song accompaniment Separation that provides a good quality of both vocal and song.