engleski

Reconstruction of Audio Recordings Using Machine Learning Techniques

Audio super resolution is still a developing field in deep learning. This paper deals with generative adversarial networks and using the adversarial training on pairs of low and high resolution audios to create a model for audio super resolution. Deep convolutional and generative adversarial networks are described in detail. A deep convolutional U-Net adversarial model is implemented, and two adversarial loss functions are tested - Wasserstein and Relativistic loss. Model is evaluated on VCTK dataset. Finally, the results and spectogram images are presented.

machine learning ; deep learning ; generative adversarial networks ; audio super resolution

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