Please use this identifier to cite or link to this item:
http://hdl.handle.net/10397/88789
DC Field | Value | Language |
---|---|---|
dc.contributor | School of Nursing | - |
dc.creator | Jiang, DZ | - |
dc.creator | Huang, DM | - |
dc.creator | Song, YY | - |
dc.creator | Wu, KC | - |
dc.creator | Lu, HK | - |
dc.creator | Liu, QQ | - |
dc.creator | Zhou, T | - |
dc.date.accessioned | 2020-12-22T01:07:58Z | - |
dc.date.available | 2020-12-22T01:07:58Z | - |
dc.identifier.uri | http://hdl.handle.net/10397/88789 | - |
dc.language.iso | en | en_US |
dc.publisher | Institute of Electrical and Electronics Engineers | en_US |
dc.rights | This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/ | en_US |
dc.rights | The following publication Jiang, D. Z., Huang, D. M., Song, Y. Y., Wu, K. C., Lu, H. K., Liu, Q. Q., & Zhou, T. (2020). An audio data representation for traffic acoustic scene recognition. IEEE Access, 8, 177863-177873 is available at https://dx.doi.org/10.1109/ACCESS.2020.3027474 | en_US |
dc.subject | Acoustics | en_US |
dc.subject | Feature extraction | en_US |
dc.subject | Spectrogram | en_US |
dc.subject | Transforms | en_US |
dc.subject | Histograms | en_US |
dc.subject | Time-Frequency analysis | en_US |
dc.subject | Visualization | en_US |
dc.subject | Feature extraction | en_US |
dc.subject | Acoustic scene recognition | en_US |
dc.subject | Transportation | en_US |
dc.subject | Acoustic material | en_US |
dc.title | An audio data representation for traffic acoustic scene recognition | en_US |
dc.type | Journal/Magazine Article | en_US |
dc.identifier.spage | 177863 | - |
dc.identifier.epage | 177873 | - |
dc.identifier.volume | 8 | - |
dc.identifier.doi | 10.1109/ACCESS.2020.3027474 | - |
dcterms.abstract | Acoustic scene recognition (ASR), recognizing acoustic environments given an audio recording of the scene, has a wide range of applications, e.g. robotic navigation and audio forensic. However, ASR remains challenging mainly due to the difficulty of representing audio data. In this article, we focus on traffic acoustic data. Traffic acoustic sense recognition provides complementary information to visual information of the scene; for example, it can be used to verify the visual perception result. The acoustic analysis and recognition, in consideration of its simple and convenient, can effectively enhance the perception ability which only applies visual information. We propose an audio data representation method to improve the traffic acoustic scene recognition accuracy. The proposed method employs the constant Q transform (CQT) and histogram of gradient (HOG) to transfer the one-dimensional audio signals into a time-frequency representation. We also propose two data representation mechanisms, called global and local feature selections, in order to select features that are able to describe the shape of time-frequency structures. We finally exploit the least absolute shrinkage and selection operator (LASSO) technique to further improve the recognition accuracy, by further selecting the most representative information for the recognition. We implemented extensive experiments, and the results show that the proposed method is effective, significantly outperforming the state-of-the-art methods. | - |
dcterms.accessRights | open access | en_US |
dcterms.bibliographicCitation | IEEE access, . . 2020, , v. 8, p. 177863-177873 | - |
dcterms.isPartOf | IEEE access | - |
dcterms.issued | 2020 | - |
dc.identifier.isi | WOS:000576244600001 | - |
dc.identifier.eissn | 2169-3536 | - |
dc.description.validate | 202012 bcrc | - |
dc.description.oa | Version of Record | en_US |
dc.identifier.FolderNumber | OA_Scopus/WOS | en_US |
dc.description.pubStatus | Published | en_US |
Appears in Collections: | Journal/Magazine Article |
Files in This Item:
File | Description | Size | Format | |
---|---|---|---|---|
Jiang_Audio_Data_Representation.pdf | 1.64 MB | Adobe PDF | View/Open |
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