Exploring Musical Roots: Applying Audio Embeddings to Empower Influence Attribution for a Generative Music Model
CoRR(2024)
摘要
Every artist has a creative process that draws inspiration from previous
artists and their works. Today, "inspiration" has been automated by generative
music models. The black box nature of these models obscures the identity of the
works that influence their creative output. As a result, users may
inadvertently appropriate, misuse, or copy existing artists' works. We
establish a replicable methodology to systematically identify similar pieces of
music audio in a manner that is useful for understanding training data
attribution. A key aspect of our approach is to harness an effective music
audio similarity measure. We compare the effect of applying CLMR and CLAP
embeddings to similarity measurement in a set of 5 million audio clips used to
train VampNet, a recent open source generative music model. We validate this
approach with a human listening study. We also explore the effect that
modifications of an audio example (e.g., pitch shifting, time stretching,
background noise) have on similarity measurements. This work is foundational to
incorporating automated influence attribution into generative modeling, which
promises to let model creators and users move from ignorant appropriation to
informed creation. Audio samples that accompany this paper are available at
https://tinyurl.com/exploring-musical-roots.
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