MusicLDM: Enhancing Novelty in Text-to-Music Generation Using
  Beat-Synchronous Mixup Strategies
Ke Chen Yusong Wu Haohe Liu Marianna Nezhurina Taylor Berg-Kirkpatrick Shlomo Dubnov
Diffusion models have shown promising results in cross-modal generation
tasks, including text-to-image and text-to-audio generation. However,
generating music, as a special type of audio, presents unique challenges due to
limited availability of music data and sensitive issues related to copyright
and plagiarism. In this paper, to tackle these challenges, we first construct a
state-of-the-art text-to-music model, MusicLDM, that adapts Stable Diffusion
and AudioLDM architectures to the music domain. We achieve this by retraining
the contrastive language-audio pretraining model (CLAP) and the Hifi-GAN
vocoder, as components of MusicLDM, on a collection of music data samples.
Then, to address the limitations of training data and to avoid plagiarism, we
leverage a beat tracking model and propose two different mixup strategies for
data augmentation: beat-synchronous audio mixup and beat-synchronous latent
mixup, which recombine training audio directly or via a latent embeddings
space, respectively. Such mixup strategies encourage the model to interpolate
between musical training samples and generate new music within the convex hull
of the training data, making the generated music more diverse while still
staying faithful to the corresponding style. In addition to popular evaluation
metrics, we design several new evaluation metrics based on CLAP score to
demonstrate that our proposed MusicLDM and beat-synchronous mixup strategies
improve both the quality and novelty of generated music, as well as the
correspondence between input text and generated music.