Altered engine recognition is of significant research and application prospects since it is a sub-task of environmental sound classification and helps detect altered vehicles for law enforcement. In this work, we propose a simple but effective framework based on a sound embedding network with an attention mechanism to identify noise emitted from vehicles with altered engines. Real-world vehicle sound data were collected at a number of streets with different amounts of traffic in Hong Kong. In particular, we developed a proprietary dataset consisting of the environmental and engine sounds through manual segmentation and annotation, and we found that the attention mechanism can help emphasize the informative segments in the frame-level embeddings outputted by the embedding network. We also demonstrate the effectiveness of the proposed attention mechanism on the ESC-50 and UrbanSound8K datasets. The experimental results show that our method achieves remarkable performance on UrbanSound8K and outperforms the baseline on ESC-50 by more than 10%.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Deep Segment-Attentive Network for Altered-Engine Recognition


    Beteiligte:


    Erscheinungsdatum :

    24.09.2023


    Format / Umfang :

    1263154 byte





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    Attentive Billboards

    Haritaoglu, I. / Flickner, M. / IEEE | British Library Conference Proceedings | 2001


    Attentive billboards

    Haritaoglu, I. / Flickner, M. | IEEE | 2001


    Pre-Attentive and Attentive Detection of Humans in Wide-Field Scenes

    Elder, J. H. / Prince, S. J. / Hou, Y. et al. | British Library Online Contents | 2007


    Attentive Systems: A Survey

    Nguyen, T. V. / Zhao, Q. / Yan, S. | British Library Online Contents | 2018


    LiDAR Imaging-based Attentive Perception

    Tsiourva, Maria / Papachristos, Christos | IEEE | 2020