pyAMPACT: A Score-Audio Alignment Toolkit for Performance Data Estimation and Multi-modal Processing

Autor:	Devaney, Johanna, McKemie, Daniel, Morgan, Alex
Rok vydání:	2024
Předmět:	Computer Science - Sound Computer Science - Multimedia Electrical Engineering and Systems Science - Audio and Speech Processing
Druh dokumentu:	Working Paper
Popis:	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-formatted 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, with timing-related information available from the score alignment. Beyond performance data estimation, pyAMPACT also facilitates multi-modal investigations through its infrastructure for linking symbolic representations and annotations to audio. Comment: International Society for Music Information Retrieval, Late Breaking Demo
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2412.05436 Zobrazit plný text záznamu View this record from Arxiv