Concept drift adaptation for audio scene classification using high-level features

Ibnu Daqiqil Id, Masanobu Abe, Sunao Hara

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

Abstract

Data used in the model training is assumed to have a similar distribution when the model is applied. However, in some applications, the data distributions may change over time. This condition, known as the concept drift, might decrease the model performance because the model is trained and evaluated in different distributions. To solve this problem in the audio scene classification task, we previously proposed the Combine-merge Gaussian mixture model (CMGMM) algorithm, where Mel-frequency cepstral coefficients (MFCCs) are used as the feature vector. In this paper, in the CMGMM algorithm, we propose to use the Pre-trained audio neural networks (PANNs) to model event audio that exists in the scene. A motivation is, instead of acoustic features, to make the best use of high-level features obtained by a model that trained using a large amount of audio data. The experiment result shows that the proposed method using PANNs improves model accuracy. In the active methods with abrupt and gradual concept drift, it is recommended to use PANNs to have significant accuracy improvement and obtain optimal adaptation results.

Original languageEnglish
Title of host publication2022 IEEE International Conference on Consumer Electronics, ICCE 2022
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781665441544
DOIs
Publication statusPublished - 2022
Event2022 IEEE International Conference on Consumer Electronics, ICCE 2022 - Virtual, Online, United States
Duration: Jan 7 2022Jan 9 2022

Publication series

NameDigest of Technical Papers - IEEE International Conference on Consumer Electronics
Volume2022-January
ISSN (Print)0747-668X

Conference

Conference2022 IEEE International Conference on Consumer Electronics, ICCE 2022
Country/TerritoryUnited States
CityVirtual, Online
Period1/7/221/9/22

Keywords

  • Audio scene classification
  • audio tagging
  • concept drift
  • gaussian mixture model

ASJC Scopus subject areas

  • Industrial and Manufacturing Engineering
  • Electrical and Electronic Engineering

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