Proposes an attribution-aware compensation framework for generative music that derives closed-form payments from catalog-level attribution informativeness and quantifies welfare effects under competition.
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7 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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DEMON is a streaming diffusion engine that exposes denoising parameters as playable controls at up to 12.3 decoder completions per second via per-slot scheduling, shared state, source blending, and accelerated decoding.
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.
MusicRFM discovers interpretable concept directions in music model hidden states using RFM probes and injects them at inference to steer generation toward desired musical properties without retraining.
LiveBand generates high-fidelity music accompaniments to live audio in real time via a causal transformer in audio latent space trained with adversarial sequence-level supervision.
An interactive system re-centers the I-Ching as a meaning-bearing framework by combining Wen Wang Fa coin casting, Gemini LLM interpretation of hexagrams, and Lyria generative music to produce responsive sonic realizations tied to user inquiry.
A latent diffusion model with consistency distillation generates real-time instrumental accompaniment from live context audio, integrated with MAX/MSP for feasible human-AI co-performance.
citing papers explorer
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DEMON: Diffusion Engine for Musical Orchestrated Noise
DEMON is a streaming diffusion engine that exposes denoising parameters as playable controls at up to 12.3 decoder completions per second via per-slot scheduling, shared state, source blending, and accelerated decoding.
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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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Steering Autoregressive Music Generation with Recursive Feature Machines
MusicRFM discovers interpretable concept directions in music model hidden states using RFM probes and injects them at inference to steer generation toward desired musical properties without retraining.
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LiveBand: Live Accompaniment Generation in the Audio Domain
LiveBand generates high-fidelity music accompaniments to live audio in real time via a causal transformer in audio latent space trained with adversarial sequence-level supervision.
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Music of Changing Lines: Toward a Culturally Situated Approach to the I-Ching
An interactive system re-centers the I-Ching as a meaning-bearing framework by combining Wen Wang Fa coin casting, Gemini LLM interpretation of hexagrams, and Lyria generative music to produce responsive sonic realizations tied to user inquiry.
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Towards Real-Time Human-AI Musical Co-Performance: Accompaniment Generation with Latent Diffusion Models and MAX/MSP
A latent diffusion model with consistency distillation generates real-time instrumental accompaniment from live context audio, integrated with MAX/MSP for feasible human-AI co-performance.