Abstract: Computer-based methods infer a metrical grid from music that has been input without a predetermined time signature or tempo, enabling such free rhythm input to be annotated with the inferred grid, and stored and transcribed as a musical score. The methods use Bayesian modeling techniques, in which an optimal metrical grid is inferred by identifying the metrical grid that best explains the given sequence of notes by maximizing the posterior probability that it represents the note sequence. Prior musical input from a given user as well as explicit information about the musical style of the input may be used to improve the accuracy of the transcription.

Abstract: Computer-based methods infer a metrical grid from music that has been input without a predetermined time signature or tempo, enabling such free rhythm input to be annotated with the inferred grid, and stored and transcribed as a musical score. The methods use Bayesian modeling techniques, in which an optimal metrical grid is inferred by identifying the metrical grid that best explains the given sequence of notes by maximizing the posterior probability that it represents the note sequence. Prior musical input from a given user as well as explicit information about the musical style of the input may be used to improve the accuracy of the transcription.