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Learning-based Cooperative Sound Event Detection with Edge Computing

Jingrong Wang,Kaiyang Liu,G. Tzanetakis,Jianping Pan

Published 2018 in IEEE International Performance, Computing, and Communications Conference

ABSTRACT

In this paper, we propose a novel real-time sound event detection framework, which combines multi-label learning and edge computing, to classify and localize abnormal sound events for city surveillance. Multiple devices equipped with acoustic sensors are deployed to collect the audio information. A learning-based approach is introduced to address the difficulties of accurately classifying the temporally overlapping acoustic events in a noisy environment. Then, edge computing is adopted to handle the high processing complexity of the learned analytics. Computation-intensive tasks of classification and localization can be offloaded to the nearby edge server for low-latency sound detection. An ensemble-based cooperative decision-making algorithm is also presented to aggregate the information from distributed devices in order to obtain better classification results. Extensive evaluations show the effectiveness of edge computing which helps reduce the time latency as well as the superiority of cooperative post-processing on the edge server to obtain a high accuracy.

PUBLICATION RECORD

  • Publication year

    2018

  • Venue

    IEEE International Performance, Computing, and Communications Conference

  • Publication date

    2018-11-01

  • Fields of study

    Computer Science, Engineering

  • Identifiers
    DOI 10.1109/PCCC.2018.8711202
  • External record

    Open on Semantic Scholar

  • Source metadata

    Semantic Scholar

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CONCEPTS

  • No concepts are published for this paper.

REFERENCES

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