PYAMPACT: A SCORE-AUDIO ALIGNMENT TOOLKIT FOR PERFORMANCE DATA ESTIMATION AND MULTI-MODAL PROCESSING

Johanna Devaney (Brooklyn College)*, Daniel McKemie (Brooklyn College), Alexander Morgan (Independent)

This paper will be presented virtually at the 12:15 PM - 12:45 PM PST session

Abstract:

pyAMPACT (Python-based Automatic Music Performance Analysis and Comparison Toolkit) links symbolic and audio music representations to facilitate score-informed estimation of performance data in audio as well as general linking of symbolic and audio music representations with a variety of annotations. pyAMPACT can read a range of symbolic formats and can output note-linked audio descriptors/performance data into MEI and Humdrum kern files. The audio analysis uses score alignment to calculate time-frequency regions of importance for each note in the symbolic representation from which to estimate a range of parameters. These include tuning-, dynamics-, and timbre- related performance descriptors, while timing-related information is available from the score alignment. Beyond performance data estimation, pyAMPACT also facilitates multi-modal investigations through its robust infrastructure for linking symbolic representations and annotations to audio.