An active learning approach to audio-to-score alignment using dynamic time warping

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Scopus citations

Abstract

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.

Original languageEnglish (US)
Title of host publicationProceedings - 2016 15th IEEE International Conference on Machine Learning and Applications, ICMLA 2016
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages796-799
Number of pages4
ISBN (Electronic)9781509061662
DOIs
StatePublished - Jan 31 2017
Externally publishedYes
Event15th IEEE International Conference on Machine Learning and Applications, ICMLA 2016 - Anaheim, United States
Duration: Dec 18 2016Dec 20 2016

Other

Other15th IEEE International Conference on Machine Learning and Applications, ICMLA 2016
CountryUnited States
CityAnaheim
Period12/18/1612/20/16

Keywords

  • Active learning
  • Audio-to-score alignment
  • Dynamic time warping

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Networks and Communications
  • Computer Science Applications

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  • Cite this

    Chuan, C-H. (2017). An active learning approach to audio-to-score alignment using dynamic time warping. In Proceedings - 2016 15th IEEE International Conference on Machine Learning and Applications, ICMLA 2016 (pp. 796-799). [7838246] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICMLA.2016.2