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

Ching-Hua Chuan

doi:10.1109/ICMLA.2016.2

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

Ching-Hua Chuan

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

4 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 language	English (US)
Title of host publication	Proceedings - 2016 15th IEEE International Conference on Machine Learning and Applications, ICMLA 2016
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	796-799
Number of pages	4
ISBN (Electronic)	9781509061662
DOIs	https://doi.org/10.1109/ICMLA.2016.2
State	Published - Jan 31 2017
Externally published	Yes
Event	15th IEEE International Conference on Machine Learning and Applications, ICMLA 2016 - Anaheim, United States Duration: Dec 18 2016 → Dec 20 2016

Other

Other	15th IEEE International Conference on Machine Learning and Applications, ICMLA 2016
Country/Territory	United States
City	Anaheim
Period	12/18/16 → 12/20/16

Keywords

Active learning
Audio-to-score alignment
Dynamic time warping

ASJC Scopus subject areas

Artificial Intelligence
Computer Networks and Communications
Computer Science Applications

Access to Document

10.1109/ICMLA.2016.2

Cite this

An active learning approach to audio-to-score alignment using dynamic time warping. / Chuan, Ching-Hua.

Proceedings - 2016 15th IEEE International Conference on Machine Learning and Applications, ICMLA 2016. Institute of Electrical and Electronics Engineers Inc., 2017. p. 796-799 7838246.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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., 7838246, Institute of Electrical and Electronics Engineers Inc., pp. 796-799, 15th IEEE International Conference on Machine Learning and Applications, ICMLA 2016, Anaheim, United States, 12/18/16. https://doi.org/10.1109/ICMLA.2016.2

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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.",

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AB - 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.

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

Abstract

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Keywords

ASJC Scopus subject areas

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