Online Domain-Incremental Learning Approach to Classify Acoustic Scenes in All Locations

TL;DR

This paper introduces an online domain-incremental learning method for acoustic scene classification that updates model statistics with minimal training, effectively preventing forgetting across multiple locations.

Contribution

It presents a novel approach that adjusts only Batch Normalization statistics for continual learning in acoustic scene classification, avoiding extensive retraining.

Findings

Outperforms fine-tuning methods in accuracy.

Achieves 48.8% average accuracy after all tasks.

Effectively prevents forgetting of previous locations.

Abstract

In this paper, we propose a method for online domain-incremental learning of acoustic scene classification from a sequence of different locations. Simply training a deep learning model on a sequence of different locations leads to forgetting of previously learned knowledge. In this work, we only correct the statistics of the Batch Normalization layers of a model using a few samples to learn the acoustic scenes from a new location without any excessive training. Experiments are performed on acoustic scenes from 11 different locations, with an initial task containing acoustic scenes from 6 locations and the remaining 5 incremental tasks each representing the acoustic scenes from a different location. The proposed approach outperforms fine-tuning based methods and achieves an average accuracy of 48.8% after learning the last task in sequence without forgetting acoustic scenes from the previously learned locations.