  Despite the central role that melody plays in music perception, it remains an
open challenge in music information retrieval to reliably detect the notes of
the melody present in an arbitrary music recording. A key challenge in melody
transcription is building methods which can handle broad audio containing any
number of instrument ensembles and musical styles - existing strategies work
well for some melody instruments or styles but not all. To confront this
challenge, we leverage representations from Jukebox (Dhariwal et al. 2020), a
generative model of broad music audio, thereby improving performance on melody
transcription by $20$% relative to conventional spectrogram features. Another
obstacle in melody transcription is a lack of training data - we derive a new
dataset containing $50$ hours of melody transcriptions from crowdsourced
annotations of broad music. The combination of generative pre-training and a
new dataset for this task results in $77$% stronger performance on melody
transcription relative to the strongest available baseline. By pairing our new
melody transcription approach with solutions for beat detection, key
estimation, and chord recognition, we build Sheet Sage, a system capable of
transcribing human-readable lead sheets directly from music audio.
  Audio examples can be found at https://chrisdonahue.com/sheetsage and code at
https://github.com/chrisdonahue/sheetsage .
