Multi-view representation for sound event recognition
Author:
Funder
Department of Science and Technology, Govt. of India
Publisher
Springer Science and Business Media LLC
Subject
Electrical and Electronic Engineering,Signal Processing
Link
https://link.springer.com/content/pdf/10.1007/s11760-020-01851-9.pdf
Reference39 articles.
1. Yang, W., Krishnan, S.: Sound event detection in real-life audio using joint spectral and temporal features. Signal Image Video Process. 12(7), 1345–1352 (2018)
2. Kong, Q., Xu, Y., Sobieraj, I., Wang, W., Plumbley, D.M.: Sound event detection and time-frequency segmentation from weakly labelled data. IEEE/ACM Trans. Audio Speech Lang. Process. (TASLP) 27(4), 777–787 (2019)
3. Chandrakala, S., Jayalakshmi, S.L.: Generative model driven representation learning in a hybrid framework for environmental audio scene and sound event recognition. IEEE Trans. Multimed. 22(1), 3–14 (2020)
4. Shreyas, N., Venkatraman, M., Malini, S., Chandrakala, S.: Trends of sound event recognition in audio surveillance: a recent review and study. In: The Cognitive Approach in Cloud Computing and Internet of Things Technologies for Surveillance Tracking Systems, pp. 95–106. Elsevier, (2020)
5. Wang, C.-Y., Tai, T.-C., Wang, J.-C., Santoso, A., Mathu-laprangsan, S., Chiang, C.-C., Chung-Hsien, W.: Sound events recognition and retrieval using multi-convolutional-channel sparse coding convolutional neural networks. IEEE/ACM Trans. Audio Speech Lang. Process. 28, 1875–1887 (2020)
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