Concept Drift Adaptation for Acoustic Scene Classifier Based on Gaussian Mixture Model [ABELAB]

sound, acoustic scene classification, Ibunu Daqiqil Id, Sunao Hara, Masanobu Abe

Concept Drift Adaptation for Acoustic Scene Classifier Based on Gaussian Mixture Model

Ibunu Daqiqil Id, Masanobu Abe, Sunao Hara
The 2020 IEEE Region 10 Conference (IEEE-TENCON 2020), pp.450–455, Online/Virtual Conference (Osaka, Japan), Nov. 2020.

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.

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