Feature Extraction of Binaural Recordings for Acoustic Scene Classification

Sławomir Zieliński, Hyunkook Lee

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

1 Citation (Scopus)

Abstract

Binaural technology becomes increasingly popular in the multimedia systems. This paper identifies a set of features of binaural recordings suitable for the automatic classification of the four basic spatial audio scenes representing the most typical patterns of audio content distribution around a listener. Moreover, it compares the five artificial-intelligence-based methods applied to the classification of binaural recordings. The results show that both the spatial and the spectro-temporal features are essential to accurate classification of binaurally rendered acoustic scenes. The spectro-temporal features appear to have a stronger influence on the classification results than the spatial metrics. According to the obtained results, the method based on the support vector machine, exploiting the features identified in the study, yields the classification accuracy approaching 84%.
Original languageEnglish
Title of host publicationIEEE Federated Conference on Computer Science and Information Systems, FedCSIS 2018
EditorsMaria Ganzha, Leszek Maciaszek, Marcin Paprzycki
PublisherIEEE
Pages585-588
Number of pages4
ISBN (Electronic)9788394941956
ISBN (Print)9781538623718
Publication statusPublished - 29 Oct 2018
EventIEEE Federated Conference on Computer Science and Information Systems
- Poznan, Poland
Duration: 9 Sep 201812 Sep 2018
https://fedcsis.org/2018/ (Link to Conference Website)

Conference

ConferenceIEEE Federated Conference on Computer Science and Information Systems
Abbreviated titleFedCSIS
CountryPoland
CityPoznan
Period9/09/1812/09/18
Internet address

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acoustics
artificial intelligence
multimedia
method

Cite this

Zieliński, S., & Lee, H. (2018). Feature Extraction of Binaural Recordings for Acoustic Scene Classification. In M. Ganzha, L. Maciaszek, & M. Paprzycki (Eds.), IEEE Federated Conference on Computer Science and Information Systems, FedCSIS 2018 (pp. 585-588). IEEE.
Zieliński, Sławomir ; Lee, Hyunkook. / Feature Extraction of Binaural Recordings for Acoustic Scene Classification. IEEE Federated Conference on Computer Science and Information Systems, FedCSIS 2018. editor / Maria Ganzha ; Leszek Maciaszek ; Marcin Paprzycki. IEEE, 2018. pp. 585-588
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abstract = "Binaural technology becomes increasingly popular in the multimedia systems. This paper identifies a set of features of binaural recordings suitable for the automatic classification of the four basic spatial audio scenes representing the most typical patterns of audio content distribution around a listener. Moreover, it compares the five artificial-intelligence-based methods applied to the classification of binaural recordings. The results show that both the spatial and the spectro-temporal features are essential to accurate classification of binaurally rendered acoustic scenes. The spectro-temporal features appear to have a stronger influence on the classification results than the spatial metrics. According to the obtained results, the method based on the support vector machine, exploiting the features identified in the study, yields the classification accuracy approaching 84{\%}.",
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Zieliński, S & Lee, H 2018, Feature Extraction of Binaural Recordings for Acoustic Scene Classification. in M Ganzha, L Maciaszek & M Paprzycki (eds), IEEE Federated Conference on Computer Science and Information Systems, FedCSIS 2018. IEEE, pp. 585-588, IEEE Federated Conference on Computer Science and Information Systems
, Poznan, Poland, 9/09/18.

Feature Extraction of Binaural Recordings for Acoustic Scene Classification. / Zieliński, Sławomir; Lee, Hyunkook.

IEEE Federated Conference on Computer Science and Information Systems, FedCSIS 2018. ed. / Maria Ganzha; Leszek Maciaszek; Marcin Paprzycki. IEEE, 2018. p. 585-588.

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

TY - GEN

T1 - Feature Extraction of Binaural Recordings for Acoustic Scene Classification

AU - Zieliński, Sławomir

AU - Lee, Hyunkook

N1 - © © 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

PY - 2018/10/29

Y1 - 2018/10/29

N2 - Binaural technology becomes increasingly popular in the multimedia systems. This paper identifies a set of features of binaural recordings suitable for the automatic classification of the four basic spatial audio scenes representing the most typical patterns of audio content distribution around a listener. Moreover, it compares the five artificial-intelligence-based methods applied to the classification of binaural recordings. The results show that both the spatial and the spectro-temporal features are essential to accurate classification of binaurally rendered acoustic scenes. The spectro-temporal features appear to have a stronger influence on the classification results than the spatial metrics. According to the obtained results, the method based on the support vector machine, exploiting the features identified in the study, yields the classification accuracy approaching 84%.

AB - Binaural technology becomes increasingly popular in the multimedia systems. This paper identifies a set of features of binaural recordings suitable for the automatic classification of the four basic spatial audio scenes representing the most typical patterns of audio content distribution around a listener. Moreover, it compares the five artificial-intelligence-based methods applied to the classification of binaural recordings. The results show that both the spatial and the spectro-temporal features are essential to accurate classification of binaurally rendered acoustic scenes. The spectro-temporal features appear to have a stronger influence on the classification results than the spatial metrics. According to the obtained results, the method based on the support vector machine, exploiting the features identified in the study, yields the classification accuracy approaching 84%.

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Zieliński S, Lee H. Feature Extraction of Binaural Recordings for Acoustic Scene Classification. In Ganzha M, Maciaszek L, Paprzycki M, editors, IEEE Federated Conference on Computer Science and Information Systems, FedCSIS 2018. IEEE. 2018. p. 585-588