LiveBand: Real-Time Music Accompaniment Generation System Achieves High-Fidelity Audio Synthesis
Researchers have developed LiveBand, a real-time system that generates musical accompaniments to live audio input while respecting strict causal constraints that prevent access to future audio frames. The system uses a causal transformer generator trained in the latent space of a pre-trained audio autoencoder with adversarial supervision. The approach improves upon prior work on audio quality, beat alignment, and mix adherence while enabling streaming generation on consumer hardware without future lookahead.
LiveBand is a real-time music accompaniment generation system designed to work with live audio input under strict causal constraints. The method trains a causal transformer generator within the continuous latent space of a pre-trained causal audio autoencoder, using adversarial sequence-level supervision from a discriminator. At each timestep, the generator receives only causally available mix context and Gaussian noise to predict accompaniment latents, without access to future mix frames or ground-truth target latents. The training and inference computations are matched by design to eliminate teacher forcing and exposure bias. On a multi-instrument music accompaniment benchmark, LiveBand demonstrates improvements over prior work on objective measures of audio quality, beat alignment, and mix adherence, while enabling real-time streaming generation on consumer hardware without requiring lookahead into future audio.
What's missing
The paper does not discuss potential limitations such as performance on musical genres or instruments not well-represented in training data, computational requirements compared to non-causal baselines, or user studies evaluating perceptual quality of generated accompaniments.
What different sources said
- arXiv cs.AICenter
LiveBand: Live Accompaniment Generation in the Audio Domain
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