A Hybrid Approach to Audio-to-Score Alignment

2020/07/28 by Ruchit Agrawal, Simon Dixon, Agrawal, Ruchit +1
Computer Science · #Audio and Speech Processing (eess.AS) #FOS: Computer and information sciences #FOS: Electrical engineering #Machine Learning (cs.LG) #Music Technology and Sound Studies #Music and Audio Processing #Sound (cs.SD) #Speech and Audio Processing #electronic engineering #information engineering

paper · pdf · doi:10.48550/arxiv.2007.14333

openalex publication_date 2020/07/28 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

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.

Citations

• Dynamic programming algorithm optimization for spoken word recognition
• Learning Features of Music from Scratch
• Towards Score Following In Sheet Music Images.
• Multi-label Music Genre Classification from Audio, Text, and Images Using Deep Features
• Onsets and Frames: Dual-Objective Piano Transcription
• Audio-to-Score Alignment using Transposition-invariant Features

Related

• Learning Frame Similarity using Siamese networks for Audio-to-Score Alignment
• Audio-to-score alignment of piano music using RNN-based automatic music transcription
• Structure-Aware Audio-to-Score Alignment using Progressively Dilated Convolutional Neural Networks
• Towards Context-Aware Neural Performance-Score Synchronisation
• Audio-to-Score Alignment Using Deep Automatic Music Transcription