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.
Title resolution pending
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
representative citing papers
XAttnMark is a new neural audio watermarking method using partial parameter sharing, cross-attention for message retrieval, temporal conditioning, and a psychoacoustic TF masking loss that reports state-of-the-art detection and attribution robustness.
citing papers explorer
-
What's a Credit Worth? A Market Framework for Attribution-Aware Compensation in Generative Music
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.
-
XAttnMark: Learning Robust Audio Watermarking with Cross-Attention
XAttnMark is a new neural audio watermarking method using partial parameter sharing, cross-attention for message retrieval, temporal conditioning, and a psychoacoustic TF masking loss that reports state-of-the-art detection and attribution robustness.