An active learning approach to audio-to-score alignment using dynamic time warping
We propose an integrated system using active learning for audio-to-score alignment. Audio-to-score alignment is a fundamental task in music information retrieval. Although various machine learning techniques have been applied to this task, it is not the case for active learning. To show how beneficial active learning is in audio-to-score alignment, we demonstrate a system that integrates it with dynamic time warping, a commonly used algorithm for time series alignment. We propose a simple parametric model for selecting queries-a crucial step in active learning. We evaluate the system using synthesized audio as well as real performances. The alignment accuracy is improved with a range from 20% to 50% using only less than 10% query instances, a promising result that hopefully can inspire the creation of a collaborative framework between human and machine for audio-to-score alignment in the future.
Proceedings - 2016 15th IEEE International Conference on Machine Learning and Applications, ICMLA 2016
Digital Object Identifier (DOI)
Ching-Hua Chuan. (2016). An Active Learning Approach to Audio-to-Score Alignment Using Dynamic Time Warping. 2016 15th IEEE International Conference on Machine Learning and Applications (ICMLA), 796–799. https://doi.org/10.1109/ICMLA.2016.0142