A novel discriminative feature extraction for acoustic scene classification using RNN based source separation

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

Research output: Contribution to journalArticlepeer-review

11 Citations (Scopus)


Various types of classifiers and feature extraction methods for acoustic scene classification have been recently proposed in the IEEE Detection and Classification of Acoustic Scenes and Events (DCASE) 2016 Challenge Task 1. The results of the final evaluation, however, have shown that even top 10 ranked teams, showed extremely low accuracy performance in particular class pairs with similar sounds. Due to such sound classes being difficult to distinguish even by human ears, the conventional deep learning based feature extraction methods, as used by most DCASE participating teams, are considered facing performance limitations. To address the low performance problem in similar class pair cases, this letter proposes to employ a recurrent neural network (RNN) based source separation for each class prior to the classification step. Based on the fact that the system can effectively extract trained sound components using the RNN structure, the mid-layer of the RNN can be considered to capture discriminative information of the trained class. Therefore, this letter proposes to use this mid-layer information as novel discriminative features. The proposed feature shows an average classification rate improvement of 2.3% compared to the conventional method, which uses additional classifiers for the similar class pair issue.

Original languageEnglish
Pages (from-to)3041-3044
Number of pages4
JournalIEICE Transactions on Information and Systems
Issue number12
Publication statusPublished - 2017 Dec

Bibliographical note

Funding Information:
This material is based upon work supported by the Air Force Office of Scientific Research under award number FA2386-16-1-4130 and authors would like to thank the anonymous reviewers for their valuable comments.

Publisher Copyright:
Copyright © 2017 The Institute of Electronics, Information and Communication Engineers.


  • Acoustic scene classification
  • Bottleneck feature
  • Recurrent neural network
  • Transfer learning

ASJC Scopus subject areas

  • Software
  • Hardware and Architecture
  • Computer Vision and Pattern Recognition
  • Electrical and Electronic Engineering
  • Artificial Intelligence


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