Bayesian Metrical Grid Inference for Free Rhythm Musical Input
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Solution Overview
Problem
Existing music recording systems require users to pre-select tempo and time signature, constraining natural performance variations and posing difficulties for less experienced users unfamiliar with music theory.
Innovation Solution
A musical input system that infers a metrical grid and tempo from free rhythm input using Bayesian modeling, allowing users to record without pre-selecting tempo or time signature, and optionally considers musical style and prior input for improved accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If the system requires pre-selection of tempo and time signature, then the system can provide structured musical notation and quantization, but the user is constrained from natural performance variations and timing variations
Solution Approach 1:
The system performs preliminary action by automatically determining tempo and time signature before the user records their performance. The processor analyzes the user's free-rhythm input and pre-determines the metrical grid, time signature, and tempo, allowing the user to record without constraints while still enabling accurate quantization and notation later.
2Reliability
If the system requires pre-selection of tempo and time signature, then the system can interpret and quantize musical data, but less experienced users face difficulty due to lack of music theory knowledge
Solution Approach 1:
The system applies self-service by automatically determining the tempo and time signature without requiring user input or music theory knowledge. The processor analyzes the user's free-rhythm performance and autonomously identifies the metrical grid, making the system accessible to users of all skill levels while maintaining accurate musical data interpretation.
3Adaptability or versatility
If the user turns off the metronome click to record unconstrained, then the user gains performance freedom, but the system cannot interpret or quantize the musical data
Solution Approach 1:
The system uses an intermediary approach by introducing a post-processing analysis stage where the processor acts as a mediator between the user's free-rhythm performance and the quantization system. The processor analyzes the unconstrained performance to infer the metrical grid and tempo, recovering the metrical structure information that would otherwise be lost, and enables accurate quantization without constraining the user during recording.
Data Source
AI summary
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.