Concept drift adaptation for acoustic scene classifier based on gaussian mixture model

Ibnu Daqiqil Id, Masanobu Abe, Sunao Hara

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

Abstract

In non-stationary environments, data might change over time, leading to variations in the underlying data distributions. This phenomenon is called concept drift and it negatively impacts the performance of scene detection models due to them being trained and evaluated on data with different distributions. This paper presents a new algorithm for detecting and adapting to concept drifts based on combining the existing and new components Gaussian mixture model then merging it. The algorithm is equipped with a drift detector based on kernel density estimation enabling the algorithm to adapt to new data and generalize over old and new concepts well.

Original languageEnglish
Title of host publication2020 IEEE Region 10 Conference, TENCON 2020
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages450-455
Number of pages6
ISBN (Electronic)9781728184555
DOIs
Publication statusPublished - Nov 16 2020
Event2020 IEEE Region 10 Conference, TENCON 2020 - Virtual, Osaka, Japan
Duration: Nov 16 2020Nov 19 2020

Publication series

NameIEEE Region 10 Annual International Conference, Proceedings/TENCON
Volume2020-November
ISSN (Print)2159-3442
ISSN (Electronic)2159-3450

Conference

Conference2020 IEEE Region 10 Conference, TENCON 2020
CountryJapan
CityVirtual, Osaka
Period11/16/2011/19/20

Keywords

  • Acoustic Scene Classification
  • Concept Drift
  • Gaussian Mixture Model
  • Incremental Learning

ASJC Scopus subject areas

  • Computer Science Applications
  • Electrical and Electronic Engineering

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