  We demonstrate that language models pre-trained on codified
(discretely-encoded) music audio learn representations that are useful for
downstream MIR tasks. Specifically, we explore representations from Jukebox
(Dhariwal et al. 2020): a music generation system containing a language model
trained on codified audio from 1M songs. To determine if Jukebox's
representations contain useful information for MIR, we use them as input
features to train shallow models on several MIR tasks. Relative to
representations from conventional MIR models which are pre-trained on tagging,
we find that using representations from Jukebox as input features yields 30%
stronger performance on average across four MIR tasks: tagging, genre
classification, emotion recognition, and key detection. For key detection, we
observe that representations from Jukebox are considerably stronger than those
from models pre-trained on tagging, suggesting that pre-training via codified
audio language modeling may address blind spots in conventional approaches. We
interpret the strength of Jukebox's representations as evidence that modeling
audio instead of tags provides richer representations for MIR.
