Audio-Based Automatic Generation of a Piano Reduction Score by Considering the Musical Structure

Abstract

This study describes a method that automatically generates a piano reduction score from the audio recordings of popular music while considering the musical structure. The generated score comprises both right- and left-hand piano parts, which reflect the melodies, chords, and rhythms extracted from the original audio signals. Generating such a reduction score from an audio recording is challenging because automatic music transcription is still considered to be inefficient when the input contains sounds from various instruments. Reflecting the long-term correlation structure behind similar repetitive bars is also challenging; further, previous methods have independently generated each bar. Our approach addresses the aforementioned issues by integrating musical analysis, especially structural analysis, with music generation. Our method extracts rhythmic features as well as melodies and chords from the input audio recording and reflects them in the score. To consider the long-term correlation between bars, we use similarity matrices, created for several acoustical features, as constraints. We further conduct a multivariate regression analysis to determine the acoustical features that represent the most valuable constraints for generating a musical structure. We have generated piano scores using our method and have observed that we can produce scores that differently balance between the ability to achieve rhythmic characteristics and the ability to obtain musical structures.

Supported by JST ACCEL, Japan (grant no. JPMJAC1602).