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- W4385006961 abstract "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." @default.
- W4385006961 created "2023-07-22" @default.
- W4385006961 creator A5030447201 @default.
- W4385006961 date "2023-07-20" @default.
- W4385006961 modified "2023-10-17" @default.
- W4385006961 title "Progressive distillation diffusion for raw music generation" @default.
- W4385006961 doi "https://doi.org/10.48550/arxiv.2307.10994" @default.
- W4385006961 hasPublicationYear "2023" @default.
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