CNN architectures for large-scale audio classification

ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings

Volumn , Issue , 2017, Pages 131-135

  Hershey, Shawn ^a,b   Chaudhuri, Sourish ^a,b   Ellis, Daniel P W ^a,b   Gemmeke, Jort F ^a,b   Jansen, Aren ^a,b   Moore, R Channing ^a,b   Plakal, Manoj ^a,b   Platt, Devin ^a,b   Saurous, Rif A ^a,b   Seybold, Bryan ^a,b   Slaney, Malcolm ^a,b   Weiss, Ron J ^a,b   Wilson, Kevin ^a,b  

^a GOOGLE INC   (United States)

^b NONE   (United States)

Author keywords

Acoustic Event Detection; Acoustic Scene Classification; Convolutional Neural Networks; Deep Neural Networks; Video Classification

Indexed keywords

EID: 85023741194     PISSN: 15206149     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICASSP.2017.7952132     Document Type: Conference Paper

References (29)

1. A. Krizhevsky, I. Sutskever, and G. E. Hinton, "Imagenet classification with deep convolutional neural networks," in Advances in neural information processing systems, 2012, pp. 1097-1105.
2. K. Simonyan and A. Zisserman, "Very deep convolutional networks for large-scale image recognition," arXiv preprint arXiv:1409.1556, 2014.
3. C. Szegedy, V. Vanhoucke, S. Ioffe, J. Shlens, and Z. Wojna, "Rethinking the inception architecture for computer vision," arXiv preprint arXiv:1512.00567, 2015.
4. K. He, X. Zhang, S. Ren, and J. Sun, "Deep residual learning for image recognition," arXiv preprint arXiv:1512.03385, 2015.
5. J. F. Gemmeke, D. P. W. Ellis, D. Freedman, A. Jansen, W. Lawrence, R. C. Moore, M. Plakal, and M. Ritter, "Audio Set: An ontology and human-labeled dartaset for audio events," in IEEE ICASSP 2017, New Orleans, 2017.
6. J. Deng, W. Dong, R. Socher, L.-J. Li, K. Li, and L. Fei-Fei, "Imagenet: A large-scale hierarchical image database," in Computer Vision and Pattern Recognition, 2009. CVPR 2009. IEEE Conference on. IEEE, 2009, pp. 248-255.
7. R. F Lyon, "Machine hearing: An emerging field [exploratory dsp]," Ieee signal processing magazine, Vol. 27, no. 5, pp. 131-139, 2010.
8. A. Mesaros, T. Heittola, A. Eronen, and T. Virtanen, "Acoustic event detection in real life recordings," in Signal Processing Conference, 201018th European. IEEE, 2010, pp. 1267-1271.
9. X. Zhuang, X. Zhou, M. A. Hasegawa-Johnson, and T. S. Huang, "Real-world acoustic event detection," Pattern Recognition Letters, Vol. 31, no. 12, pp. 1543-1551, 2010.
10. J. F Gemmeke, L. Vuegen, P. Karsmakers, B. Vanrumste, et al., "An exemplar-based nmf approach to audio event detection," in 2013 IEEE Workshop on Applications of Signal Processing to Audio and Acoustics. IEEE, 2013, pp. 1-4.
11. A. Temko, R. Malkin, C. Zieger, D. Macho, C. Nadeu, and M. Omologo, "Clear evaluation of acoustic event detection and classification systems," in International Evaluation Workshop on Classification of Events, Activities and Relationships. Springer, 2006, pp. 311-322.
12. N. Takahashi, M. Gygli, B. Pfister, and L. Van Gool, "Deep convolutional neural networks and data augmentation for acoustic event detection," arXiv preprint arXiv:1604.07160, 2016.
13. G. Parascandolo, H. Huttunen, and T. Virtanen, "Recurrent neural networks for polyphonic sound event detection in real life recordings," in 2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2016, pp. 6440-6444.
14. G. Awad, J. Fiscus, M. Michel, D. Joy, W. Kraaij, A. F Smeaton, G. Quenot, M. Eskevich, R. Aly, and R. Ordelman, "Trecvid 2016: Evaluating video search, video event detection, localization, and hyperlinking," in Proceedings of TRECVID 2016. NIST, USA, 2016.
15. B. G. Fabian Caba Heilbron, Victor Escorcia and J. C. Niebles, "Activitynet: A large-scale video benchmark for human activity understanding," in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2015, pp. 961-970.
16. A. Karpathy, G. Toderici, S. Shetty, T. Leung, R. Sukthankar, and L. Fei-Fei, "Large-scale video classification with convolutional neural networks," in CVPR, 2014.
17. A. Mesaros, T. Heittola, and T. Virtanen, "TUT database for acoustic scene classification and sound event detection," in 24th European Signal Processing Conference 2016 (EUSIPCO 2016), Budapest, Hungary, 2016, http://www.cs.tut.fi/sgn/arg/dcase2016/.
18. T. N. Sainath, O. Vinyals, A. Senior, and H. Sak, "Convolutional, long short-term memory, fully connected deep neural networks," in 2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2015, pp. 4580-4584.
19. H. Eghbal-Zadeh, B. Lehner, M. Dorfer, and G. Widmer, "Cp-jku submissions for dcase-2016: A hybrid approach using binaural i-vectors and deep convolutional neural networks,".
20. J. Yue-Hei Ng, M. Hausknecht, S. Vijayanarasimhan, O. Vinyals, R. Monga, and G. Toderici, "Beyond short snippets: Deep networks for video classification," in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2015, pp. 4694-4702.
21. A. Kumar and B. Raj, "Audio event detection using weakly labeled data," arXiv preprint arXiv:1605.02401, 2016.
22. A. Singhal, "Introducing the knowledge graph: things, not strings," 2012, Official Google blog, https://googleblog.blogspot.com/2012/05/introducing-knowledge-graph-things-not.html.
23. M. Abadi et al., "TensorFlow: Large-scale machine learning on heterogeneous systems," 2015, Software available from ten-sorflow.org.
24. D. Kingma and J. Ba, "Adam: A method for stochastic optimization," arXiv preprint arXiv:1412.6980, 2014.
25. S. Ioffe and C. Szegedy, "Batch normalization: Accelerating deep network training by reducing internal covariate shift," arXiv preprint arXiv:1502.03167, 2015.
26. N. Srivastava, G. E. Hinton, A. Krizhevsky, I. Sutskever, and R. Salakhutdinov, "Dropout: a simple way to prevent neural networks from overfitting.," Journal of Machine Learning Research, Vol. 15, no. 1, pp. 1929-1958, 2014.
27. T. Fawcett, "Roc graphs: Notes and practical considerations for researchers," Machine learning, Vol. 31, no. 1, pp. 1-38, 2004.
28. C. Buckley and E. M. Voorhees, "Retrieval evaluation with incomplete information," in Proceedings of the 27th annual international ACM SIGIR conference on Research and development in information retrieval. ACM, 2004, pp. 25-32.
29. V. Nair and G. E. Hinton, "Rectified linear units improve restricted boltzmann machines," in Proceedings of the 27th International Conference on Machine Learning (ICML-10), 2010, pp. 807-814.