Performance evaluation of acoustic scene classification using DNN-GMM and frame-concatenated acoustic features

Gen Takahashi, Takeshi Yamada, Nobutaka Ono, Shoji Makino

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Citations (Scopus)

Abstract

We previously proposed a method of acoustic scene classification using a deep neural network-Gaussian mixture model (DNN-GMM) and frame-concatenated acoustic features. It was submitted to the Detection and Classification of Acoustic Scenes and Events (DCASE) 2016 Challenge and was ranked eighth among 49 algorithms. In the proposed method, acoustic features in temporally distant frames were concatenated to capture their temporal relationship. The experimental results indicated that the classification accuracy is improved by increasing the number of concatenated frames. On the other hand, the frame concatenation interval, which is the interval with which the frames used for frame concatenation are selected, is another important parameter. In our previous method, the frame concatenation interval was fixed to 100 ms. In this paper, we optimize the number of concatenated frames and the frame concatenation interval for the previously proposed method. As a result, it was confirmed that the classification accuracy of the method was improved by 2.61% in comparison with the result submitted to the DCASE 2016.

Original languageEnglish
Title of host publicationProceedings - 9th Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2017
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1739-1743
Number of pages5
ISBN (Electronic)9781538615423
DOIs
Publication statusPublished - 2018 Feb 5
Externally publishedYes
Event9th Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2017 - Kuala Lumpur, Malaysia
Duration: 2017 Dec 122017 Dec 15

Publication series

NameProceedings - 9th Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2017
Volume2018-February

Other

Other9th Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2017
CountryMalaysia
CityKuala Lumpur
Period17/12/1217/12/15

ASJC Scopus subject areas

  • Artificial Intelligence
  • Human-Computer Interaction
  • Information Systems
  • Signal Processing

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