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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2412.05436 |
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| _version_ | 1866917184134971392 |
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| author | Devaney, Johanna McKemie, Daniel Morgan, Alex |
| author_facet | Devaney, Johanna McKemie, Daniel Morgan, Alex |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_05436 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | pyAMPACT: A Score-Audio Alignment Toolkit for Performance Data Estimation and Multi-modal Processing Devaney, Johanna McKemie, Daniel Morgan, Alex Sound Multimedia Audio and Speech Processing 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. |
| title | pyAMPACT: A Score-Audio Alignment Toolkit for Performance Data Estimation and Multi-modal Processing |
| topic | Sound Multimedia Audio and Speech Processing |
| url | https://arxiv.org/abs/2412.05436 |