A Hybrid Approach to Audio-to-Score Alignment
This is an incremental improvement for music information retrieval, addressing alignment accuracy in diverse acoustic settings.
The paper tackled audio-to-score alignment by using neural networks as a preprocessing step for Dynamic Time Warping (DTW) methods, resulting in robust alignments adaptable to various acoustic conditions.
Audio-to-score alignment aims at generating an accurate mapping between a performance audio and the score of a given piece. Standard alignment methods are based on Dynamic Time Warping (DTW) and employ handcrafted features. We explore the usage of neural networks as a preprocessing step for DTW-based automatic alignment methods. Experiments on music data from different acoustic conditions demonstrate that this method generates robust alignments whilst being adaptable at the same time.