Deep Neural Network based learning and transferring mid-level audio features for acoustic scene classification

Seongkyu Mun; Suwon Shon; Wooil Kim; David K. Han; Hanseok Ko

ICASSP 2017accepted0 citations

Deep Neural Network based learning and transferring mid-level audio features for acoustic scene classification

Seongkyu Mun, Suwon Shon, Wooil Kim, David K. Han, Hanseok Ko

Abstract

Deep Neural Network (DNN) based transfer learning has been shown to be effective in Visual Object Classification (VOC) for complementing the deficit of target domain training samples by adapting classifiers that have been pre-trained for other large-scaled DataBase (DB). Although there exists an abundance of acoustic data, it can also be said that datasets of specific acoustic scenes are sparse for training Acoustic Scene Classification (ASC) models. By exploiting VOC DNN's ability of learning beyond its pre-trained environments, this paper proposes DNN based transfer learning for ASC. Effectiveness of the proposed method is demonstrated on the database of IEEE DCASE Challenge 2016 Task 1 and home surveillance environment via representative experiments. Its improved performance is verified by comparing it to prominent conventional methods.

BibTeX

@inproceedings{icassp2017_deepneuralnetwor,
  title = {Deep Neural Network based learning and transferring mid-level audio features for acoustic scene classification},
  author = {Seongkyu Mun and Suwon Shon and Wooil Kim and David K. Han and Hanseok Ko},
  booktitle = {ICASSP 2017},
  year = {2017}
}