Active Learning for Efficient Audio Annotation and Classification with a Large Amount of Unlabeled Data

Yu Wang, Ana Elisa Mendez Mendez, Mark Cartwright, Juan Pablo Bello

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

Abstract

There are many sound classification problems that have target classes which are rare or unique to the context of the problem. For these problems, existing data sets are not sufficient and we must create new problem-specific datasets to train classification models. However, annotating a new dataset for every new problem is costly. Active learning could potentially reduce this annotation cost, but it has been understudied in the context of audio annotation. In this work, we investigate active learning to reduce the annotation cost of a sound classification dataset unique to a particular problem. We evaluate three certainty-based active learning query strategies and propose a new strategy: alternating confidence sampling. Using this strategy, we demonstrate reduced annotation costs when actively training models with both experts and non-experts, and we perform a qualitative analysis on 20k unlabeled recordings to show our approach results in a model that generalizes well to unseen data.

Original languageEnglish (US)
Title of host publication2019 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2019 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages880-884
Number of pages5
ISBN (Electronic)9781479981311
DOIs
StatePublished - May 2019
Event44th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2019 - Brighton, United Kingdom
Duration: May 12 2019May 17 2019

Publication series

NameICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
Volume2019-May
ISSN (Print)1520-6149

Conference

Conference44th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2019
CountryUnited Kingdom
CityBrighton
Period5/12/195/17/19

Keywords

  • active learning
  • audio annotations
  • machine listening
  • sound classification

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Electrical and Electronic Engineering

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