Acoustic Scene Classifier Based on Gaussian Mixture Model in the Concept Drift Situation

Abstract

The data distribution used in model training is assumed to be similar with that when the model is applied. However, in some applications, data distributions may change over time. This situation is called the concept drift, which might decrease the model performance because the model is trained and evaluated in different distributions. To solve this problem for scene audio classification, this study proposes the kernel density drift detection (KD3) algorithm to detect the concept drift and the combine-merge Gaussian mixture model (CMGMM) algorithm to adapt to the concept drift. The strength of the CMGMM algorithm is its ability to perform adaptation and continuously learn from stream data with a local replacement strategy that enables it to preserve previously learned knowledge and avoid catastrophic forgetting. KD3 plays an essential role in detecting the concept drift and supplying adaptation data to the CMGMM. Their performance is evaluated for four types of concept drift with three systematically generated scenarios. The CMGMM is evaluated with and without the concept drift detector. In summary, the combination of the CMGMM and KD3 outperforms two of four other combination methods and shows its best performance at a recurring concept drift.