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Spatial Audio Scene Characterization (SASC)

Automatic Classification of Five-Channel Surround Sound Recordings According to the Foreground and Background Content

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Multimedia and Network Information Systems (MISSI 2018)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 833))

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Abstract

Spatial audio becomes increasingly popular in domestic and mobile multimedia applications. Evaluating quality of experience (QoE) of such applications requires the development of algorithms capable of identification and quantification of perceptual characteristics of spatial audio scenes. This paper introduces a method for the automatic categorization of surround sound recordings using a criterion based on the distribution of foreground and background audio content around a listener. The principles of the method were demonstrated using a study in which a corpus of 110 five-channel surround sound recordings was computationally classified according to the two basic spatial audio scene categories. In order to develop the proposed method a novel metric, representing spatial audio characteristics, was identified. Moreover, five machine learning algorithms, including neural networks, random forests and support vector machines, were employed and their performance compared. According to the obtained results, the proposed method was capable of categorization of surround sound recordings reaching accuracy of 99%.

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Acknowledgments

This work was supported by a grant S/WI/3/2013 from Bialystok University of Technology and funded from the resources for research by Ministry of Science and Higher Education.

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Authors and Affiliations

  1. Faculty of Computer Science, Białystok University of Technology, Białystok, Poland

    Sławomir K. Zieliński

Authors
  1. Sławomir K. Zieliński
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Correspondence to Sławomir K. Zieliński .

Editor information

Editors and Affiliations

  1. Faculty of Computer Science and Management, Wrocław University of Science and Technology, Wrocław, Poland

    Kazimierz Choroś

  2. Faculty of Computer Science and Management, Wrocław University of Science and Technology, Wrocław, Poland

    Marek Kopel

  3. Faculty of Computer Science and Management, Wrocław University of Science and Technology, Wrocław, Poland

    Elżbieta Kukla

  4. Faculty of Computer Science and Management, Wrocław University of Science and Technology, Wrocław, Poland

    Andrzej Siemiński

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Zieliński, S.K. (2019). Spatial Audio Scene Characterization (SASC). In: Choroś, K., Kopel, M., Kukla, E., Siemiński, A. (eds) Multimedia and Network Information Systems. MISSI 2018. Advances in Intelligent Systems and Computing, vol 833. Springer, Cham. https://doi.org/10.1007/978-3-319-98678-4_46

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