@ARTICLE{Suruthhi_V._S._Detection_2021,
 author={Suruthhi, V. S. and Smita, V. and Gini J., Rolant and Ramachandran, K.I.},
 volume={vol. 67},
 number={No 4},
 journal={International Journal of Electronics and Telecommunications},
 pages={735-741},
 howpublished={online},
 year={2021},
 publisher={Polish Academy of Sciences Committee of Electronics and Telecommunications},
 abstract={Safety and security have been a prime priority in people’s lives, and having a surveillance system at home keeps people and their property more secured. In this paper, an audio surveillance system has been proposed that does both the detection and localization of the audio or sound events. The combined task of detecting and localizing the audio events is known as Sound Event Localization and Detection (SELD). The SELD in this work is executed through Convolutional Recurrent Neural Network (CRNN) architecture. CRNN is a stacked layer of convolutional neural network (CNN), recurrent neural network (RNN) and fully connected neural network (FNN). The CRNN takes multichannel audio as input, extracts features and does the detection and localization of the input audio events in parallel. The SELD results obtained by CRNN with the gated recurrent unit (GRU) and with long short-term memory (LSTM) unit are compared and discussed in this paper. The SELD results of CRNN with LSTM unit gives 75% F1 score and 82.8% frame recall for one overlapping sound. Therefore, the proposed audio surveillance system that uses LSTM unit produces better detection and overall performance for one overlapping sound.},
 type={Article},
 title={Detection and Localization of Audio Event for Home Surveillance Using CRNN},
 URL={http://ochroma.man.poznan.pl/Content/121913/PDF-MASTER/101_2705_Suruthhi_skl2.pdf},
 doi={10.24425/ijet.2021.139771},
 keywords={convolutional recurrent neural network (CRNN), gated recurrent unit (GRU), long short-term memory (LSTM), sound event localization and detection (SELD)},
}