Polyphonic sound event detection using multi label deep neural networks

Proceedings of the International Joint Conference on Neural Networks

Volumn 2015-September, Issue , 2015, Pages

  Cakir, Emre ^a   Heittola, Toni ^a   Huttunen, Heikki ^a   Virtanen, Tuomas ^a  

^a NONE

Author keywords

Speech

Indexed keywords

CLASSIFICATION (OF INFORMATION); FACTORIZATION; HIDDEN MARKOV MODELS; MARKOV PROCESSES; SPEECH;

DEEP NEURAL NETWORKS; MULTI LABEL CLASSIFICATION; NONNEGATIVE MATRIX FACTORIZATION; OVERALL ACCURACIES; POLYPHONIC SOUNDS; REALISTIC ENVIRONMENTS; SOUND EVENT DETECTION; STATE-OF-THE-ART METHODS;

AUDIO RECORDINGS;

EID: 84951103511     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/IJCNN.2015.7280624     Document Type: Conference Paper

References (19)

1. T Heittola, A. Mesaros, T Virtanen, and M. Gabbouj, "Supervised model training for overlapping sound events based on unsupervised source separation," in Int. Corif. Acoustics, Speech, and Signal Processing (ICASSP), Vancouver, Canada, 2013, pp. 8677-8681.
2. D. Zhang and D. Ellis, "Detecting sound events in basketball video archive," Dept. Electronic Eng., Columbia Univ., New York, 2001.
3. S. Chu, S. Narayanan, C. Kuo, and M. J. Mataric, "Where am I scene recognition for mobile robots using audio features," in IEEE Int. Corif. Multimedia and Expo (ICME). IEEE, 2006, pp. 885-888.
4. A. Harma, M. E McKinney, and J. Skowronek, "Automatic surveillance of the acoustic activity in our living environment," in TEEE Tnt. Conl Multimedia and Expo (ICME). IEEE, 2005, pp. 4-pp.
5. A. Mesaros, T Heittola, A. Eronen, and T Virtanen, "Acoustic event detection in real life recordings," in Proc. European Signal Processing Conference (EUSTPCG), 2010, pp. 1267-1271.
6. T Heittola, A. Mesaros, A. Eronen, and T Virtanen, "Contextdependent sound event detection," EURASIP Journal on Audio, Speech, and Music Processing, vol. 2013, no. I, pp. 1,2013.
7. O. Dikmen and A. Mesaros, "Sound event detection using nonnegative dictionaries learned from annotated overlapping events," in IEEE Workshop on Applications of Signal Processing to Audio and Acoustics (WASPAA). IEEE, 2013, pp. 1-4.
8. J. Dennis, H. D. Tran, and E. S. Chng, "Overlapping sound event recognition using local spectrogram features and the generalised hough transform," Pattern Recognition Letters, vol. 34, no. 9, pp. 1085-1093, 2013.
9. M. Zhang and Z. Zhou, "Multi label neural networks with applications to functional genomics and text categorization," IEEE Trans. Knowledge and Data Engineering, vol. 18, no. 10, pp. 1338-1351, 2006.
10. M. R. Boutell, J. Luo, X. Shen, and C. M. Brown, "Learning multilabel scene classification," Pattern recognition, vol. 37, no. 9, pp. 1757-1771, 2004.
11. A. McCallum, "Multi-label text classification with a mixture model trained by EM," in Workshop on Text Learning, 1999, pp. 1-7.
12. O. Gencoglu, T Virtanen, and H. Huttunen, "Recognition of acoustic events using deep neural networks," in Proc. European Signal Processing Coriference (EUSIPCG), 2014.
13. G. Hinton, L. Deng, D. Yu, G. E. Dahl, A. Mohamed, N. Jaitly, A. Senior, V. Vanhoucke, P. Nguyen, and TN. Sainath, "Deep neural networks for acoustic modeling in speech recognition: The shared views of four research groups," Signal Processing Magazine, IEEE, vol. 29, no. 6, pp. 82-97, 2012.
14. Pawel Swietojanski, Jinyu Li, and Jui-Ting Huang, "Investigation of max out networks for speech recognition," in Tnt. Conl Acoustics, Speech, and Signal Processing (TCASSP), 2014, pp. 7649-7653.
15. P.-S. Huang, M. Kim, M. Hasegawa-Johnson, and P. Smaragdis, "Deep learning for monaural speech separation," in Tnt. Conl Acoustics, Speech, and Signal Processing (TCASSP), 2014, pp. 1562-1566.
16. Y. Bengio, "Learning deep architectures for AI," Foundations and trends in Machine Learning, vol. 2, no. I, pp. 1-127, 2009.
17. U. Goodfellow, D. Warde-Farley, M. Mirza, A. Courville, and Y. Bengio, "Maxout networks," in Proc. Int. Corif. Machine Learning (TCML), 2013, pp. 1319-1327.
18. G. E. Dahl, TN. Sainath, and G. E. Hinton, "Improving deep neural networks for LVCSR using rectified linear units and dropout," in Tnt. Conl Acoustics, Speech, and Signal Processing (TCASSP). IEEE, 2013, pp. 8609-8613.
19. S. Kullback and R. A. Leibler, "On information and sufficiency," T he Annals of Mathematical Statistics, pp. 79-86, 1951.