Multi-label UAV sound classification using Stacked Bidirectional LSTM

Dana Utebayeva, Akhan Almagambetov, Manal Alduraibi, Yelmurat Temirgaliyev, Lyazzat Ilipbayeva, Sungat Marxuly

Research output: Chapter in Book/Report/Conference proceedingConference contribution

10 Citations (Scopus)


Nowadays Unmanned Aerial Vehicles (UAVs) pose an increasing threat to public areas such as parks, schools, hospitals and official buildings. Different methods of dealing with UAV detection are developing more and more actively. This paper primarily focuses on two key aims: the first aim is to perform a multi-label classification system and the second aim is to develop Stacked Bidirectional Long Short-Term Memory (LSTM) with two hidden layers to categorize multiple UAVs sounds. Frame-wise spectral-domain features are applied as inputs of the proposed system. Overall, the results of the study show that the sound of UAVs can be classified into multiple labels. This study has been one of the first attempts to thoroughly examine Stacked Bidirectional LSTM for UAV sound classification task.
Original languageEnglish
Title of host publication2020 Fourth IEEE International Conference on Robotic Computing (IRC)
Number of pages6
Publication statusPublished - Nov 1 2020


  • Computer architecture
  • Unmanned aerial vehicles
  • Noise measurement
  • Task analysis
  • Spectral analysis
  • Robots
  • Load modeling
  • sound classification
  • UAV sound classification
  • Multi label classification
  • LSTM
  • Stacked Bidirectional LSTM


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