LiveBand: 音频域中的实时伴奏生成
LiveBand: Live Accompaniment Generation in the Audio Domain
AI总结：
提出LiveBand系统，利用因果Transformer在预训练因果音频自编码器的连续潜空间中生成高保真伴奏，通过对抗序列级监督训练，实现实时流式生成。
摘要：
我们提出LiveBand，一个实时系统，能够为现场音频输入生成高保真音乐伴奏，并严格遵守因果约束。我们的方法在预训练因果音频自编码器的连续潜空间中训练因果Transformer生成器，使用来自判别器的对抗序列级监督。在每个时间步，生成器仅接收因果可用的混合上下文和高斯噪声，并预测伴奏潜变量，而无法访问未来混合帧或真实目标潜变量。训练在因果掩码下通过单个并行前向传播完成，而流式推理则通过滚动注意力状态自回归进行。模型训练和推理计算在设计中匹配，消除了教师强制及相关曝光偏差。在多乐器音乐伴奏基准测试中，LiveBand在音频质量、节拍对齐和混合一致性的客观指标上优于先前工作，同时能够在消费级硬件上实现无需前瞻的实时流式生成。
英文摘要：
We present LiveBand, a real-time system that generates high-fidelity music accompaniments to live audio input, respecting strict causal constraints. Our method trains a causal transformer generator in 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 the causally available mix context and Gaussian noise, and predicts accompaniment latents without access to future mix frames or ground-truth target latents. Training is performed in a single parallel forward pass under causal masking, while streaming inference proceeds autoregressively with a rolling attention state. The model's training and inference computations are matched by design, eliminating teacher forcing and the associated exposure bias. On a multi-instrument music accompaniment benchmark, LiveBand improves over prior work on objective measures of audio quality, beat alignment, and mix adherence, while enabling real-time streaming generation without lookahead into the future on consumer hardware.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
