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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
TL;DR
Text-to-music generation is limited by scarce music data and plagiarism concerns. The paper introduces MusicLDM with beat-synchronous audio and latent mixup strategies, finding improved quality, novelty, and text-music correspondence while identifying BLM as the standout strategy. Its scope remains limited by 16 kHz audio, scarce real text-music data, and constrained scaling evidence.
Problem
Text-to-music generation lacks abundant parallel training data and faces copyright-related plagiarism concerns.
Method
MusicLDM adapts Stable Diffusion and AudioLDM to music and uses beat-synchronous audio and latent mixup for data augmentation.
Results
MusicLDM with mixup improves musical quality, novelty, and text-music correspondence, while BLM is identified as the standout mixup strategy.
Takeaways & Limitations
Beat-synchronous mixup provides a music-specific augmentation strategy for generating novel text-conditioned music from limited training data.
Takeaways & Limitations
MusicLDM uses 16 kHz audio, cannot yet be scaled with limited real text-music data and GPU resources, and its mixup trends are not established beyond Audiostock.
Abstract
from arXiv · showhide
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.
1 Introduction
Text-to-music generation is constrained by scarce text-music training pairs and concerns about plagiarism, motivating MusicLDM and beat-synchronous mixup strategies. The proposed system improves musical quality, novelty, and text-music correspondence while training on 9K pairs.
- Challenges: Text-to-music generation faces limited parallel training data and risks plagiarism because music contains nuanced structure and is often copyright-protected.These constraints make high-quality conditional modeling and novel generation difficult.
- Approach: MusicLDM adapts Stable Diffusion and AudioLDM to generate novel text-conditioned musical audio from limited parallel training data.The paper presents both a new model and a training strategy for the task.
- Approach: Mixup recombines existing training points through linear interpolation, encouraging interpolation between musical examples rather than memorization.The paper applies mixup to address data limitations and plagiarism concerns in music generation.
- Results: Beat-synchronous mixup strategies substantially reduce copying while improving musical audio quality and correspondence between generated audio and input text.The strategies encourage generation within the convex hull of the training data.
- Results: MusicLDM with mixup outperforms state-of-the-art text-to-music models in automatic evaluations and human listening tests using 9K text-music sample pairs.The reported gains cover overall musical audio quality and text-audio correspondence.
2 Related Work
Related work establishes text-to-audio generation and diffusion models as foundations for MusicLDM, while highlighting limited reproducibility, copyright risks, and the need for music-specific mixup.
- Text-to-Audio Generation: Text-to-audio generation creates audio content from textual input, with recent systems including Diffsound, AudioGen, AudioLDM, and Make-an-Audio.Music-specific systems include retrieval-, language-model-, and diffusion-based approaches.
- Text-to-Audio Generation: Many recent text-to-audio and text-to-music models lack open-source training code and rely on inaccessible privately owned music data.This limits researchers’ ability to reproduce and build upon prior work.
- Text-to-Audio Generation: MusicLDM bases its architecture on AudioLDM, which combines Stable Diffusion, CLAP, and HiFi-GAN.The architecture is adapted for text-to-music generation.
- Plagiarism on Diffusion Models: Diffusion models can memorize or recombine training content into replicas, making plagiarism especially concerning for copyright-protected music.The paper motivates strategies to mitigate plagiarism in text-to-music generation.
- Mixup on Data Augmentation: Mixup linearly combines training samples to create intermediate feature-space examples and encourage more continuous, robust decision boundaries.Music mixup requires balancing musical elements, and diffusion models can mix latent features rather than signals.
3 Methodology
MusicLDM adapts AudioLDM-style latent diffusion for text-to-music generation, retraining key components for music and addressing limited paired data through beat-synchronous mixup. BAM mixes aligned audio signals, while BLM mixes their latent representations before decoding them back to audio.
- MusicLDM: MusicLDM combines CLAP, a VAE-based audio latent diffusion model, and a Hifi-GAN vocoder for text-to-music generation.Its latent diffusion model uses a UNet with conditioning injected into encoder and decoder blocks through FiLM.
- MusicLDM: The training pipeline derives audio embeddings, text embeddings, and VAE latent representations from each waveform and its corresponding text.The waveform is converted to a mel-spectrogram before VAE encoding, producing the latent representation used by diffusion training.
- MusicLDM: MusicLDM retrains CLAP on text-music pairs because the original model was dominated by sound-event, sound-effect, and natural-sound data.The authors also modify the conditioning strategy to address the difficulty of relying only on audio embeddings when paired text-music data is limited.
- Beat-Synchronous Mixup: Beat Transformer groups tracks by tempo and aligns their downbeats before mixup, reducing chaotic mixtures caused by mismatched musical timing.Tracks are mixed only within the same tempo group, with a selected downbeat used as the aligned starting position.
- Beat-Synchronous Mixup: BAM linearly mixes aligned audio tracks, then derives the mixed CLAP embedding and audio latent for standard MusicLDM training.The mixing ratio is sampled from a beta distribution, λ ∼ B(5, 5).
- Beat-Synchronous Mixup: BLM linearly mixes the VAE latents of aligned tracks, decodes the result into a mel-spectrogram, and vocodes it into mixed audio for training.This places interpolation in the VAE-transformed latent space before mapping the result back to audio.
- Beat-Synchronous Mixup: BAM provides smooth interpolation in audio feature space, whereas BLM aims to keep augmented samples within the learned music manifold.BLM requires VAE decoding and Hifi-GAN processing, and may be ineffective when the VAE latent space is ill-defined or collapsed.
4 Experiments
The experiments evaluate MusicLDM, its training variants, beat-synchronous mixup strategies, and CLAP-based measures of quality, relevance, novelty, and plagiarism risk. MusicLDM and especially beat-synchronous latent mixup improve the balance between generation quality, text-audio relevance, and novelty.
- Experimental design: The experiments retrain CLAP for music, train MusicLDM variants, compare mixup strategies and baselines, and evaluate objective and subjective outcomes.The evaluation covers generation quality, CLAP-based relevance and novelty, plagiarism risk, and listening-test judgments.
- Generation quality: MusicLDM outperforms retrained AudioLDM on FDpann, FDvgg, and IS, indicating that music-pretrained CLAP provides more suitable conditioning embeddings.The comparison isolates the CLAP model used for condition embeddings.
- Generation quality: Combined audio-to-audio training and text-to-audio fine-tuning substantially improves FDvgg, while audio-only or text-only training performs less favorably.The authors attribute the combination to early audio reconstruction followed by alignment toward text-conditioned generation.
- Mixup strategies: Beat-synchronous latent mixup is the most effective strategy because latent mixing preserves musicality while supporting generalization and reducing copying.Simple mixup degrades quality, while BAM partly offsets this through tempo and downbeat alignment; BLM further improves quality with text fine-tuning.
- Text-audio relevance and novelty: MusicLDM without mixup reaches the highest text-audio relevance at 0.281 but also the highest nearest-neighbor audio similarity ratio, indicating greater copying risk.BAM and BLM better balance relevance and similarity ratios while maintaining stronger generation quality than simple mixup.
- Subjective listening test: The listening test finds BAM and BLM more relevant and higher quality than MuBERT and original MusicLDM, with BLM identified as most effective overall.Fifteen subjects rated quality, relevance, and musicality across six groups of generations.
5 Limitations
The study is constrained by low-resolution audio, limited data and compute, uncertain dataset generality, and a narrow synchronization design.
- Audio resolution: Training at 16 kHz, rather than the 44.1 kHz standard for most music production, limits practical text-to-music application.The authors connect this constraint to subpar Hifi-GAN performance at high sampling rates.
- Data and compute: Limited real text-music data and GPU resources prevent scaling MusicLDM and leave mixup trends unverified beyond the Audiostock dataset.The authors state that they cannot determine whether similar trends hold elsewhere.
- Synchronization: The study focuses on beat alignment, while key-signature, instrument, and alternative audio-space filtering strategies remain unexplored.These are identified as directions for future improvement in selecting suitable music pairs for mixing.
6 Conclusion
The paper introduces MusicLDM with two beat-synchronous mixup strategies and evaluates its quality, text-music relevance, and novelty. The experiments identify beat-synchronous latent mixup as the standout strategy.
- MusicLDM incorporates CLAP, VAE, Hifi-GAN, and latent diffusion models for text-to-music generation.
- Beat-synchronous audio mixup and beat-synchronous latent mixup are integrated into MusicLDM training.
- The evaluations use objective and subjective metrics to assess musical quality, text-music relevance, and novelty.
- Beat-synchronous latent mixup is identified as an effective strategy for text-to-music generation.
A MusicLDM Details
MusicLDM uses a convolutional VAE and latent diffusion model to represent and generate music, with Hifi-GAN used for audio synthesis. The section also specifies training configurations for its components and comparison models.
- The VAE uses a four-block downsampling encoder and four-block upsampling decoder with downsampling and upsampling rate P = 8 and latent dimension C = 16.
- The latent diffusion model contains four encoder blocks, one bottleneck block, and four decoder blocks with residual CNN and spatial transformer layers.
- Hifi-GAN uses its official configuration with the number of mel-bins changed to 128.
- VAE, Hifi-GAN, and MusicLDM training use Adam or AdamW optimizers with component-specific batch sizes and learning rates.
- Riffusion and MuBERT generations are produced using their official APIs.
B.1 Hyperparameters
The CLAP component uses HTS-AT-base for audio and RoBERTa-base for text, while the appendix also includes a heading for training-set music tracks and a spectrogram figure about similar music pairs.
- CLAP uses HTS-AT-base as its audio encoder and RoBERTa-base as its text encoder.
- The appendix includes a section labeled “Music Tracks in the Training Set.”
- Figure 4 presents spectrograms of music pairs selected for high cosine similarity in CLAP audio embeddings.
- The CLAP audio and text embeddings each have dimension D = 512.
B.2 Training Details
CLAP is trained with a large batch size using Adam, warm-up, and cosine learning-rate decay.
- CLAP training uses batch size 2304 and the Adam optimizer with β1 = 0.99 and β2 = 0.9.
- The CLAP learning rate uses warm-up and cosine decay from a basic learning rate of 1 × 10^-4.
B.3 Zero-shot Classification Performance
The retrained CLAP is evaluated on zero-shot audio and music classification benchmarks. It performs best on UrbanSound8K and VGGSound, while remaining comparable on ESC-50 and on par on GTZAN.
- The retrained CLAP achieves the best acoustic-event classification performance on UrbanSound8K and VGGSound.
- Performance remains comparable on ESC-50 and on par with prior models on GTZAN music classification.
- The authors suggest that additional music training data may improve the representation space used by text-to-music generation.
C Nearest-Neighbor Audio Similarity Samples
The analysis visualizes generated music against nearest training-set segments using CLAP audio-embedding similarity. Low similarity corresponds to visibly different spectrogram structures, supporting CLAP-based novelty assessment.
- The visualizations compare generated music with nearest training-set segments using cosine similarity between CLAP audio embeddings.
- Training tracks are divided into 10-second segments, and the segment most similar to each generated query track is selected.
- Low CLAP cosine similarity corresponds to substantial spectrogram differences between generated and training music.
- These comparisons support using CLAP embeddings to assess track similarity, novelty, and possible plagiarism.
D Subjective Listening Test
The subjective listening test compares MusicLDM variants and MuBERT through randomized online ratings of short generated songs. Participants evaluate relevance, quality, and musicality while providing demographic and musical-background metadata.
- Participants rate six groups of generated songs, each containing outputs from MusicLDM, BAM, BLM, and MuBERT for the same text description.
- The song order within each group is shuffled to reduce positional bias during rating.
- Ratings measure relevance to the text description, overall audio quality, and musicality including rhythm, melodies, and textures.
- Each song lasts approximately 10 seconds and includes fade-in and fade-out, while higher scores indicate better quality.
- Negative Impacts: The model is presented as potentially supporting creativity and cultural preservation while raising concerns about job displacement, copyright, misuse, and cultural homogenization.
- Negative Impacts: Responsible deployment requires balancing potential benefits with ethical, social, and economic consequences through collective effort.