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This paper aims to apply a new deep learning approach to the task ofgenerating raw audio files. It is based on diffusion models, a recent type ofdeep generative model. This new type of method has recently shown outstandingresults with image generation. A lot of focus has been given to those models bythe computer vision community. On the other hand, really few have been givenfor other types of applications such as music generation in waveform domain.  In this paper the model for unconditional generating applied to music isimplemented: Progressive distillation diffusion with 1D U-Net. Then, acomparison of different parameters of diffusion and their value in a fullresult is presented. One big advantage of the methods implemented through thiswork is the fact that the model is able to deal with progressing audioprocessing and generating , using transformation from 1-channel 128 x 384 to3-channel 128 x 128 mel-spectrograms and looped generation. The empiricalcomparisons are realized across different self-collected datasets.

Progressive distillation diffusion for raw music generation