将乐谱与音频对齐，自动分析演奏表现特征
pyAMPACT: A Score-Audio Alignment Toolkit for Performance Data Estimation and Multi-modal Processing
- 通过乐谱对齐定位音频中每个音符的关键时频区域
- 可估计音高、力度、音色等演奏参数，支持时间信息提取
- 适合音乐表演分析与多模态音乐研究者使用
pyAMPACT（基于Python的自动音乐表演分析与比较工具包）连接乐谱与音频表示，实现音符级演奏数据的声学估计及多种注释的跨模态关联。该工具支持多种符号化格式读取，并能将音符关联的音频描述/演奏数据输出为MEI格式文件。音频分析基于乐谱对齐，从符号表示中每个音符对应的时频重要区域估算多项参数，包括音高、力度、音色相关表现特征，时间信息则来自乐谱对齐结果。除演奏数据估计外，pyAMPACT还提供多模态研究的基础设施，支持符号表示与各类注释在音频上的关联。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。