This paper aims to apply a new deep learning approach to the task of generating raw audio files. It is based on diffusion models, a recent type of deep generative model. This new type of method has recently shown outstanding results with image generation. A lot of focus has been given to those models by the computer vision community. On the other hand, really few have been given for other types of applications such as music generation in waveform domain. In this paper the model for unconditional generating applied to music is implemented: Progressive distillation diffusion with 1D U-Net. Then, a comparison of different parameters of diffusion and their value in a full result is presented. One big advantage of the methods implemented through this work is the fact that the model is able to deal with progressing audio processing and generating , using transformation from 1-channel 128 x 384 to 3-channel 128 x 128 mel-spectrograms and looped generation. The empirical comparisons are realized across different self-collected datasets.

翻译：本文旨在将一种新的深度学习方法应用于原始音频文件的生成任务。该方法基于扩散模型，这是一种近期出现的深度生成模型类型。这类新方法近年来在图像生成领域取得了卓越成果，计算机视觉界对此类模型给予了大量关注。然而，在波形域音乐生成等其他应用类型上，相关研究仍然较少。本文实现了应用于音乐的無条件生成模型：基于一维U-Net的渐进式蒸馏扩散。随后，本文对比了不同扩散参数及其在完整结果中的价值。本研究实现方法的一大优势在于，模型能够通过将单通道128×384数据转换为三通道128×128梅尔频谱图并进行循环生成，从而处理渐进式音频处理与生成过程。实证比较在多个自采集数据集上完成。