# Progressive distillation diffusion for raw music generation

arXiv: https://arxiv.org/abs/2307.10994 · HTML: https://mlsanity.com/paper/2307.10994/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 18 of its 23 references that are in the map.
- First posted on arXiv 20 Jul 2023.

## Builds on (most cited first, 18 of 18)

- [Improved Techniques for Training GANs](https://mlsanity.com/paper/1606.03498/) (2016)
- [WaveNet: A Generative Model for Raw Audio](https://mlsanity.com/paper/1609.03499/) (2016)
- [DiffWave: A Versatile Diffusion Model for Audio Synthesis](https://mlsanity.com/paper/2009.09761/) (2020)
- [Demystifying MMD GANs](https://mlsanity.com/paper/1801.01401/) (2018)
- [WaveGrad: Estimating Gradients for Waveform Generation](https://mlsanity.com/paper/2009.00713/) (2020)
- [AudioLDM: Text-to-Audio Generation with Latent Diffusion Models](https://mlsanity.com/paper/2301.12503/) (2023)
- [A Note on the Inception Score](https://mlsanity.com/paper/1801.01973/) (2018)
- [Noise2Music: Text-conditioned Music Generation with Diffusion Models](https://mlsanity.com/paper/2302.03917/) (2023)
- [GANSynth: Adversarial Neural Audio Synthesis](https://mlsanity.com/paper/1902.08710/) (2019)
- [SampleRNN: An Unconditional End-to-End Neural Audio Generation Model](https://mlsanity.com/paper/1612.07837/) (2016)
- [BinauralGrad: A Two-Stage Conditional Diffusion Probabilistic Model for Binaural Audio Synthesis](https://mlsanity.com/paper/2205.14807/) (2022)
- [The Zero Resource Speech Challenge 2019: TTS without T](https://mlsanity.com/paper/1904.11469/) (2019)
- [NU-Wave 2: A General Neural Audio Upsampling Model for Various Sampling Rates](https://mlsanity.com/paper/2206.08545/) (2022)
- [Musika! Fast Infinite Waveform Music Generation](https://mlsanity.com/paper/2208.08706/) (2022)
- ['Zhores' -- Petaflops supercomputer for data-driven modeling, machine learning and artificial intelligence installed in Skolkovo Institute of Science and Technology](https://mlsanity.com/paper/1902.07490/) (2019)
- [ArchiSound: Audio Generation with Diffusion](https://mlsanity.com/paper/2301.13267/) (2023)
- [Unconditional Audio Generation with Generative Adversarial Networks and Cycle Regularization](https://mlsanity.com/paper/2005.08526/) (2020)
- [Closed-form Expressions for Maximum Mean Discrepancy with Applications to Wasserstein Auto-Encoders](https://mlsanity.com/paper/1901.03227/) (2019)

## Abstract (arXiv)

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.