Audio style conversion using deep learning

Style transfer is one of the most popular uses of neural networks. It has been thoroughly researched, such as extracting the style from famous paintings and applying it to other images thus creating synthetic paintings. Generative adversarial networks (GANs) are used to achieve this. This paper explores the many ways in which the same results can be achieved with audio related tasks, for which a plethora of new applications can be found. Analysis of different techniques used to transfer styles of audios, specifically changing the gender of the audio is implemented. The Crowd sourced high-quality UK and Ireland English Dialect speech data set was used. In this paper, the input is the waveforms belonging to a male or female and waveforms belonging to the opposite gender is synthesized by the network, with the content spoken remaining the same. Different architectures are explored, from naive techniques and directly training audio waveforms against convolution neural networks (CNN) to using extensive algorithms