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Deep Learning Based Audio Scene Classification

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Computational Intelligence, Cyber Security and Computational Models. Models and Techniques for Intelligent Systems and Automation (ICC3 2017)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 844))

Abstract

Acoustic Scene Classification (ASC) is defined as recognition and categorizing an audio signal that identifies the environment in which it has been produced. This work aims to develop a Deep Neural Network (DNN) based system to detect the real life environments by analyzing their sound data. Log Mel band features are used to represent the characteristics of the input audio scenes. The parameters of the DNN are set according to the DNN baseline of DCASE 2017 challenge. The system is evaluated with TUT dataset (2017) and the result is compared with the baseline provided. The evaluation of proposed model shows an accuracy of 82%, which is better than the baseline system.

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Authors and Affiliations

  1. Department of Computer Science and Engineering, Annamalai University, Annamalainagar, India

    E. Sophiya

  2. Department of Information Technology, Annamalai University, Annamalainagar, India

    S. Jothilakshmi

Authors
  1. E. Sophiya
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  2. S. Jothilakshmi
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Corresponding author

Correspondence to E. Sophiya .

Editor information

Editors and Affiliations

  1. PSG College of Technology, Coimbatore, India

    Geetha Ganapathi

  2. BITS Pilani, KK Birla, Goa, India

    Arumugam Subramaniam

  3. University of the Basque Country, San Sebastian, Spain

    Manuel Graña

  4. PSG College of Technology, Coimbatore, India

    Suresh Balusamy

  5. PSG College of Technology, Coimbatore, India

    Rajamanickam Natarajan

  6. PSG College of Technology, Coimbatore, India

    Periakaruppan Ramanathan

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Sophiya, E., Jothilakshmi, S. (2018). Deep Learning Based Audio Scene Classification. In: Ganapathi, G., Subramaniam, A., Graña, M., Balusamy, S., Natarajan, R., Ramanathan, P. (eds) Computational Intelligence, Cyber Security and Computational Models. Models and Techniques for Intelligent Systems and Automation. ICC3 2017. Communications in Computer and Information Science, vol 844. Springer, Singapore. https://doi.org/10.1007/978-981-13-0716-4_9

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  • DOI: https://doi.org/10.1007/978-981-13-0716-4_9

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-13-0715-7

  • Online ISBN: 978-981-13-0716-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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