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2018

[1]
E BENETOS, D STOWELL, and M PLUMBLEY. Approaches to complex sound scene analysis. In T Virtanen, M PLUMBLEY, and D Ellis, editors, Computational Analysis of Sound Scenes and Events, number 8 in Signals & Communication, pages 215--242. Springer International Publishing, 1 edition, Jan 2018. [ bib | DOI | http ]
[2]
K Choi, G Fazekas, M Sandler, and K Cho. The effects of noisy labels on deep convolutional neural networks for music tagging. IEEE Transactions on Emerging Topics in Computational Intelligence, X(X (in press)), 2018. date-added: 2017-12-21 19:00:24 +0000 date-modified: 2017-12-21 19:15:46 +0000 keywords: evaluation, music tagging, deep learning, CNN bdsk-url-1: https://arxiv.org/pdf/1706.02361.pdf bdsk-url-2: https://dx.doi.org/10.1109/TETCI.2017.2771298. [ bib | DOI | .pdf ]
[3]
A Mesaros, T Heittola, E Benetos, P Foster, M Lagrange, T Virtanen, and M Plumbley. Detection and classification of acoustic scenes and events: Outcome of the dcase 2016 challenge. IEEE/ACM Transactions on Audio, Speech and Language Processing, 26:379--393, Feb 2018. [ bib | DOI | http ]
[4]
M PANTELI, E BENETOS, and S DIXON. A review of manual and computational approaches for the study of world music corpora. Journal of New Music Research, 47:176--189, Jan 2018. [ bib | DOI ]
[5]
S SKACH, A XAMBO, L TURCHET, A Stolfi, RL STEWART, and MHE BARTHET. Embodied interactions with e-textiles and the internet of sounds for performing arts. Stockholm, Sweden, Mar 2018. [ bib | DOI ]
[6]
T STOCKMAN and S Wilkie. Perception of objects that move in depth, using ecologically valid audio cues. Applied Acoustics, Jan 2018. [ bib ]

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