Title

Detection and Localization of Audio Event for Home Surveillance Using CRNN

Journal title

International Journal of Electronics and Telecommunications

Yearbook

2021

Volume

vol. 67

Issue

No 4

Affiliation

Suruthhi, V. S. : Department of Electronics and Communication Engineering, Amrita School of Engineering, Coimbatore, Amrita Vishwa Vidyapeetham, India ; Smita, V. : Department of Electronics and Communication Engineering, Amrita School of Engineering, Coimbatore, Amrita Vishwa Vidyapeetham, India ; Gini J., Rolant : Department of Electronics and Communication Engineering, Amrita School of Engineering, Coimbatore, Amrita Vishwa Vidyapeetham, India ; Ramachandran, K.I. : Centre for Computational Engineering &Networking (CEN), Amrita School of Engineering, Coimbatore, Amrita Vishwa Vidyapeetham, India

Authors

Suruthhi, V. S. ; Smita, V. ; Gini J., Rolant ; Ramachandran, K.I.

Keywords

convolutional recurrent neural network (CRNN) ; gated recurrent unit (GRU) ; long short-term memory (LSTM) ; sound event localization and detection (SELD)

Divisions of PAS

Nauki Techniczne

Coverage

735-741

Publisher

Polish Academy of Sciences Committee of Electronics and Telecommunications

Bibliography

[1] UNODC: United Nations Office on Drugs and Crimes, “Burglary | Statistics and data,” 2017. https://dataunodc.un.org/crime/burglary. [2] K. Lashmi and A. S. Pillai, “Ambient Intelligence and IoT Based Decision Support System for Intruder Detection,” 2019 IEEE International Conference on Electrical, Computer and Communication Technologies (ICECCT), Coimbatore, India, 2019, pp. 1-4. https://doi.org/10.1109/ICECCT.2019.8869327 [3] Dr. P. Prakash, R. Suresh and P.N. Kumar Dhinesh, “Smart City Video Surveillance using Fog Computing,” in International Journal of Enterprise Network Management, vol. 10, no. 3/4, pp.389 – 399, 2019. https://doi.org/10.1504/IJENM.2019.103165 [4] Caught on camera, “Different Types of CCTV-CCTV Camera Types and Uses,” 2020. [Online]. Available: https://www.caughtoncamera.net/news/different-types-of-cctv/ . [5] S. Ntalampiras, “Audio Surveillance,” 2012. [pdf]. Available: https://www.itpress.com/Secure/elibrary/papers/9781845645625/9781845645625012FU1.pdf [6] P. Foggia, N. Petkov, A. Saggese, N. Strisciuglio and M. Vento, “Audio Surveillance of Roads: A System for Detecting Anomalous Sounds,” in IEEE Transactions on Intelligent Transportation Systems, vol. 17, no. 1, pp. 279-288, Jan. 2016. https://doi.org/10.1109/TITS.2015.2470216 [7] S. Ntalampiras, I. Potamitis and N. Fakotakis, “Probabilistic Novelty Detection for Acoustic Surveillance Under Real-World Conditions,” in IEEE Transactions on Multimedia, vol. 13, no. 4, pp. 713-719, Aug. 2011. https://doi.org/10.1109/TMM.2011.2122247 [8] A. Mesaros et al., “Detection and Classification of Acoustic Scenes and Events: Outcome of the DCASE 2016 Challenge,” in IEEE/ACM Transactions on Audio, Speech, and Language Processing, vol. 26, no. 2, pp. 379-393, Feb. 2018. https://doi.org/10.1109/TASLP.2017.2778423 [9] E. Çakır, G. Parascandolo, T. Heittola, H. Huttunen and T. Virtanen, “Convolutional Recurrent Neural Networks for Polyphonic Sound Event Detection,” in IEEE/ACM Transactions on Audio, Speech, and Language Processing, vol. 25, no. 6, pp. 1291-1303, June 2017. https://doi.org/10.1109/TASLP.2017.2690575 [10] S. Adavanne, P. Pertilä and T. Virtanen, “Sound event detection using spatial features and convolutional recurrent neural network,” 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), New Orleans, LA, 2017, pp. 771-775. https://doi.org/10.1109/ICASSP.2017.7952260 [11] P. Zinemanas, P. Cancela and M. Rocamora, “End-to-end Convolutional Neural Networks for Sound Event Detection in Urban Environments,” 2019 24th Conference of Open Innovations Association (FRUCT), Moscow, Russia, 2019, pp. 533-539. https://doi.org/10.23919/FRUCT.2019.8711906 [12] G. Parascandolo, H. Huttunen and T. Virtanen, “Recurrent neural networks for polyphonic sound event detection in real-life recordings,” 2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Shanghai, 2016, pp. 6440-6444. https://doi.org/10.1109/ICASSP.2016.7472917 [13] L. Birnie, T. D. Abhayapala, H. Chen and P. N. Samarasinghe, “Sound Source Localization in a Reverberant Room Using Harmonic Based Music,” ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Brighton, United Kingdom, 2019, pp. 651-655. https://doi.org/10.1109/ICASSP.2019.8683098 [14] L. O. Nunes et al., “A Steered-Response Power Algorithm Employing Hierarchical Search for Acoustic Source Localization Using Microphone Arrays,” in IEEE Transactions on Signal Processing, vol. 62, no. 19, pp. 5171-5183, Oct.1, 2014. https://doi.org/10.1109/TSP.2014.2336636 [15] M. W. Hansen, J. R. Jensen and M. G. Christensen, “Pitch and TDOA-based localization of acoustic sources with distributed arrays,” 2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Brisbane, QLD, 2015, pp. 2664-2668. https://doi.org/10.1109/ICASSP.2015.7178454 [16] J. Pak and J. W. Shin, “Sound Localization Based on Phase Difference Enhancement Using Deep Neural Networks,” in IEEE/ACM Transactions on Audio, Speech, and Language Processing, vol. 27, no. 8, pp. 1335-1345, Aug. 2019. https://doi.org/10.1109/TASLP.2019.2919378 [17] S. Adavanne, A. Politis and T. Virtanen, “Direction of Arrival Estimation for Multiple Sound Sources Using Convolutional Recurrent Neural Network,” 2018 26th European Signal Processing Conference (EUSIPCO), Rome, 2018, pp. 1462-1466, https://doi.org/10.23919/EUSIPCO.2018.8553182

Date

2021.12.27

Type

Article

Identifier

DOI: 10.24425/ijet.2021.139771