Evaluating High-Resolution Piano Sustain Pedal Depth Estimation with Musically Informed Metrics
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arXiv
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| Autori principali: | , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866911417139986432 |
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| author | Zhang, Hanwen Fang, Kun Wang, Ziyu Fujinaga, Ichiro |
| author_facet | Zhang, Hanwen Fang, Kun Wang, Ziyu Fujinaga, Ichiro |
| contents | Evaluation for continuous piano pedal depth estimation tasks remains incomplete when relying only on conventional frame-level metrics, which overlook musically important features such as direction-change boundaries and pedal curve contours. To provide more interpretable and musically meaningful insights, we propose an evaluation framework that augments standard frame-level metrics with an action-level assessment measuring direction and timing using segments of press/hold/release states and a gesture-level analysis that evaluates contour similarity of each press-release cycle. We apply this framework to compare an audio-only baseline with two variants: one incorporating symbolic information from MIDI, and another trained in a binary-valued setting, all within a unified architecture. Results show that the MIDI-informed model significantly outperforms the others at action and gesture levels, despite modest frame-level gains. These findings demonstrate that our framework captures musically relevant improvements indiscernible by traditional metrics, offering a more practical and effective approach to evaluating pedal depth estimation models. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_03750 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Evaluating High-Resolution Piano Sustain Pedal Depth Estimation with Musically Informed Metrics Zhang, Hanwen Fang, Kun Wang, Ziyu Fujinaga, Ichiro Information Retrieval Sound Audio and Speech Processing Evaluation for continuous piano pedal depth estimation tasks remains incomplete when relying only on conventional frame-level metrics, which overlook musically important features such as direction-change boundaries and pedal curve contours. To provide more interpretable and musically meaningful insights, we propose an evaluation framework that augments standard frame-level metrics with an action-level assessment measuring direction and timing using segments of press/hold/release states and a gesture-level analysis that evaluates contour similarity of each press-release cycle. We apply this framework to compare an audio-only baseline with two variants: one incorporating symbolic information from MIDI, and another trained in a binary-valued setting, all within a unified architecture. Results show that the MIDI-informed model significantly outperforms the others at action and gesture levels, despite modest frame-level gains. These findings demonstrate that our framework captures musically relevant improvements indiscernible by traditional metrics, offering a more practical and effective approach to evaluating pedal depth estimation models. |
| title | Evaluating High-Resolution Piano Sustain Pedal Depth Estimation with Musically Informed Metrics |
| topic | Information Retrieval Sound Audio and Speech Processing |
| url | https://arxiv.org/abs/2510.03750 |