Live Music Diffusion Models adapt bidirectional diffusion for interactive music generation via KV caching and ARC-Forcing, recovering and exceeding discrete autoregressive efficiency while enabling post-training alignment without RL.
The song describer dataset: a corpus of audio captions for music-and-language evaluation.arXiv:2311.10057
7 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
representative citing papers
BandTok tokenizes Mel-spectrograms as independent time-frequency band tokens from a single codebook and pairs it with 2D RoPE in an autoregressive model to improve music generation over residual multi-codebook tokenizers.
TWNM framework equips audio-language models with spatial scene analysis via FOA simulation and metadata-grounded training, reaching 70.8% accuracy on a new ASA benchmark.
MECAT is a multi-expert benchmark for audio AI offering fine-grained captions and QA pairs generated via expert models and LLM reasoning, paired with the DATE metric that combines semantic similarity and cross-sample discriminability to favor detailed outputs.
A data-free streaming consistency distillation framework enables single-step autoregressive generation from text-to-music models for real-time interactive use while preserving timbre and rhythm via latent, spectral, and temporal losses.
STAR-VAE introduces topology-aware regularization to reshape VAE latent geometry for audio, claiming to resolve the Rate-Distortion-Regularity Trilemma and achieve SOTA reconstruction.
citing papers explorer
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Live Music Diffusion Models: Efficient Fine-Tuning and Post-Training of Interactive Diffusion Music Generators
Live Music Diffusion Models adapt bidirectional diffusion for interactive music generation via KV caching and ARC-Forcing, recovering and exceeding discrete autoregressive efficiency while enabling post-training alignment without RL.
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Modeling Music as a Time-Frequency Image: A 2D Tokenizer for Music Generation
BandTok tokenizes Mel-spectrograms as independent time-frequency band tokens from a single codebook and pairs it with 2D RoPE in an autoregressive model to improve music generation over residual multi-codebook tokenizers.
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The World is Not Mono: Enabling Spatial Understanding in Large Audio-Language Models
TWNM framework equips audio-language models with spatial scene analysis via FOA simulation and metadata-grounded training, reaching 70.8% accuracy on a new ASA benchmark.
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MECAT: A Multi-Experts Constructed Benchmark for Fine-Grained Audio Understanding Tasks
MECAT is a multi-expert benchmark for audio AI offering fine-grained captions and QA pairs generated via expert models and LLM reasoning, paired with the DATE metric that combines semantic similarity and cross-sample discriminability to favor detailed outputs.
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Real-Time Interactive Music Generation via Data-Free Streaming Consistency Distillation
A data-free streaming consistency distillation framework enables single-step autoregressive generation from text-to-music models for real-time interactive use while preserving timbre and rhythm via latent, spectral, and temporal losses.
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STAR-VAE: Structured Topology-Aware Regularization for Audio Reconstruction and Generation
STAR-VAE introduces topology-aware regularization to reshape VAE latent geometry for audio, claiming to resolve the Rate-Distortion-Regularity Trilemma and achieve SOTA reconstruction.
- A Survey of Advancing Audio Super-Resolution and Bandwidth Extension from Discriminative to Generative Models