ACOUSTIC SCENE CLASSIFICATION: A COMPETITION REVIEW

@article{Gharib2018ACOUSTICSC,
  title={ACOUSTIC SCENE CLASSIFICATION: A COMPETITION REVIEW},
  author={S. Gharib and Honain Derrar and Daisuke Niizumi and Tuukka Senttula and Janne Tommola and Toni Heittola and T. Virtanen and H. Huttunen},
  journal={2018 IEEE 28th International Workshop on Machine Learning for Signal Processing (MLSP)},
  year={2018},
  pages={1-6}
}
  • S. Gharib, Honain Derrar, +5 authors H. Huttunen
  • Published 2018
  • Engineering, Computer Science, Mathematics
  • 2018 IEEE 28th International Workshop on Machine Learning for Signal Processing (MLSP)
  • In this paper we study the problem of acoustic scene classification, i.e., categorization of audio sequences into mutually exclusive classes based on their spectral content. [...] Key Method We identify the most suitable methods and study the impact of each by performing an ablation study of the mixture of approaches. We also compare the results with a neural network baseline, and show the improvement over that. Finally, we discuss the impact of using a competition as a part of a university course, and justify…Expand Abstract

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    References

    Publications referenced by this paper.
    SHOWING 1-10 OF 26 REFERENCES
    Detection and Classification of Acoustic Scenes and Events: Outcome of the DCASE 2016 Challenge
    128
    The Machine Learning Approach for Analysis of Sound Scenes and Events
    12
    Audio-based context recognition
    377
    TUT database for acoustic scene classification and sound event detection
    368
    Very Deep Convolutional Networks for Large-Scale Image Recognition
    37726
    DCASE 2017 Challenge setup: Tasks, datasets and baseline system
    286
    ImageNet Classification with Deep Convolutional Neural Networks
    50271