Yifan Ding
ABSTRACT
Audio-to-score alignment is one of the music information retrieval (MIR) tasks that concerns the real world time when notes appeared in a corresponding audio. Although recent studies based on synthesizing MIDI to audio then applying audio feature extraction techniques and DTW-based alignment have achieved about 10 milliseconds in mean alignment error for piano music, evaluation in a real-world scenario for robustness is preferable. In this paper, we implemented a standard DTW-based Audio-to-score alignment system with audio feature extraction techniques for musical onset enhancement, and evaluated the robustness in a real-world scenario, namely for MIR database building. Considering this type of usage, we used 3 different synthesizers and real-world performance data from CrestMusePEDB in order to simulate the absence of prior information about audio recording conditions and velocity information. As for result, velocity from real-world performance and the choice of synthesizer can ruin DTW-based alignment system by almost doubling the average mean error in most cases. We also made a practical attempt at combining phase-based onset feature extraction and conventional MIDI-audio alignment framework on real-world flute aligning, indicating the protentional benefits of combining different type of audio features.
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Index Terms
- Audio Feature Extraction for DTW-based Audio-to-Score Alignment
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