DCASE 2017 Challenge setup: Tasks, datasets and baseline system

Conference object English OPEN
Mesaros , Annamaria; Heittola , Toni; Diment , Aleksandr; Elizalde , Benjamin; Shah , Ankit; Vincent , Emmanuel; Raj , Bhiksha; Virtanen , Tuomas;
(2017)
  • Publisher: HAL CCSD
  • Subject: Sound event detection | [ INFO.INFO-TS ] Computer Science [cs]/Signal and Image Processing | [INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing | Sound scene analysis | Acoustic scene classification | Audio tagging | Weak labels | Rare sound events

International audience; DCASE 2017 Challenge consists of four tasks: acoustic scene classification , detection of rare sound events, sound event detection in real-life audio, and large-scale weakly supervised sound event detection for smart cars. This paper presents the... View more
  • References (29)
    29 references, page 1 of 3

    [2] A. J. Eronen, V. T. Peltonen, J. T. Tuomi, A. P. Klapuri, S. Fagerlund, T. Sorsa, G. Lorho, and J. Huopaniemi, “Audiobased context recognition,” IEEE Trans. on Audio, Speech, and Language Processing, vol. 14, no. 1, pp. 321-329, Jan 2006.

    [3] D. Stowell and D. Clayton, “Acoustic event detection for multiple overlapping similar sources,” in IEEE Workshop on Applications of Signal Processing to Audio and Acoustic (WASPAA), October 2015.

    [4] S. Goetze, J. Schro¨der, S. Gerlach, D. Hollosi, J. Appell, and F. Wallhoff, “Acoustic monitoring and localization for social care,” Journal of Computing Science and Engineering, vol. 6, no. 1, pp. 40-50, March 2012.

    [5] D. Stowell, D. Giannoulis, E. Benetos, M. Lagrange, and M. D. Plumbley, “Detection and classification of acoustic scenes and events,” IEEE Trans. on Multimedia, vol. 17, no. 10, pp. 1733-1746, October 2015.

    [6] T. Virtanen, A. Mesaros, T. Heittola, M. Plumbley, P. Foster, E. Benetos, and M. Lagrange, Proceedings of the Detection and Classification of Acoustic Scenes and Events 2016 Workshop (DCASE2016). Tampere University of Technology. Department of Signal Processing, 2016.

    [7] D. Barchiesi, D. Giannoulis, D. Stowell, and M. Plumbley, “Acoustic scene classification: Classifying environments from the sounds they produce,” IEEE Signal Processing Magazine, vol. 32, no. 3, pp. 16-34, May 2015.

    [8] J.-J. Aucouturier, B. Defreville, and F. Pachet, “The bag-offrames approach to audio pattern recognition: A sufficient model for urban soundscapes but not for polyphonic music,” The Journal of the Acoustical Society of America, vol. 122, no. 2, pp. 881-891, 2007.

    [9] T. Heittola, A. Mesaros, A. Eronen, and T. Virtanen, “Audio context recognition using audio event histograms,” in 18th European Signal Processing Conference, Aug 2010, pp. 1272- 1276.

    [10] A. Rakotomamonjy and G. Gasso, “Histogram of gradients of time-frequency representations for audio scene classification,” IEEE/ACM Trans. Audio, Speech and Lang. Proc., vol. 23, no. 1, pp. 142-153, Jan. 2015.

    [11] B. Elizalde, A. Kumar, A. Shah, R. Badlani, E. Vincent, B. Raj, and I. Lane, “Experiments on the DCASE challenge 2016: Acoustic scene classification and sound event detection in real life recording,” in DCASE2016 Workshop on Detection and Classification of Acoustic Scenes and Events, 2016.

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