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Latent audio diffusion for music generation with expressive control

Alfred Pichard, Hugo Audas, Paul Triana, Tom Oviste

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Project on Github

Introduction

Recent advances in generative deep learning models provide exciting new tools for music generation. In particular, the conditioning capabilities of diffusion models offer interesting possibilities to add expressive control to the generation process, which helps make it a more accessible tool.

In this project, we apply the novel diffusion method Iterative $\alpha$-(de)Blending HBC23, which simplifies the usual formalism of stochastic diffusion models to a deterministic one, so as to generate audio loops from pure noise. We use the EnCodec DCSA20 high fidelity neural autoencoder to generate latent codes of a compressed audio representation. Conditioning is applied using either beats-per-minute information or high-level audio concepts, and reinforced using classifier-free guidance HS22. The latent codes are then inverted back to a waveform with the decoder.

Finally, we assess the quality of our method on a large dataset of minimal house and techno music.

Architecture

The project is currently composed of 3 independant branches, which aim to be merged in the future into a single main one. Each branch corresponds to a different approach of conditioning we decided to apply, but share a common base :

  • We rely on EnCodec DCSA20 Encoder/Decoder, trained on our dataset to train our model with the latent representation of our preferred audio dataset in a lighter format.
  • We use a UNet architecture for our Iterative $\alpha$-(de)Blending process HBC23.

UNet architecture png

(See code contained in ’/src’ for further details on each module)

Conditioning

Conditioning is applied separately in the corresponding branches with CLAP WCZ+23 (branch link) and a beat-per-minute audio descriptor (branch link)

Results

BPM

To get a learnable representation of rhythm, we use a beat-tracking information extracted from our audio samples metadata during pre-processing. Inference is done using a similar process : to get a more musically coherent output, we condition the input of our model with a constant BPM value that we transform into time signatures then a saw-tooth signal. Results are unequivocal as we can easily hear the influence of our conditioning on our generated audios.

Generated loops without bpm conditioning

Generated loops with bpm conditioning

  • 122 BPM :

  • 125 BPM :

  • 128 BPM :

CLAP

When training the model on conditioning with CLAP latent codes, the model learns to map the CLAP dual-modal latent space to the EnCodec latent space. Because EnCodec has both an encoder and a decoder, the combined process of deblending and decoding can be viewed as a decoder for the CLAP latent space. This can be used as a text-to-music process or can be very useful for applying transformations directly in the CLAP latent space.

  • Original Audio (WARNING LOUD AUDIO) :

  • Generated Audio Conditioned on CLAP :

References

  • HBC23 Eric Heitz, Laurent Belcour, and Thomas Chambon. Iterative alpha-(de)blending: a minimalist deterministic diffusion model. arXiv preprint arXiv:2305.03486, 2023.
  • DCSA20 Alexandre Défossez, Jade Copet, Gabriel Synnaeve, and Yossi Adi. High fidelity neural audio compression. preprint arXiv:2210.13438, 2020.
  • HS22 Jonathan Ho and Tim Salimans. Classifier-free diffusion guidance. arXiv preprint arXiv:2207.12598, 2022.
  • WCZ+23 Yusong Wu, Ke Chen, Tianyu Zhang, Yuchen Hui, Taylor Berg-Kirkpatrick, and Shlomo Dubnov. Large-scale contrastive language-audio pretraining with feature fusion and keyword-to-caption augmentation. ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pages 1–5, 2023.