Online Domain-Incremental Learning Approach to Classify Acoustic Scenes in All Locations
<|reference_start|>Online Domain-Incremental Learning Approach to Classify Acoustic Scenes in All Locations: 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.<|reference_end|>
@article{mulimani2024online,
    title={Online Domain-Incremental Learning Approach to Classify Acoustic Scenes
  in All Locations},
    author={Manjunath Mulimani, Annamaria Mesaros},
    journal={arXiv preprint arXiv:2406.13386},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.13386},
    primaryClass={eess.AS cs.SD}
}