B. Lehner, Hamid Eghbal-Zadeh, Matthias Dorfer,
"Classifying Short Acoustic Scenes with I-Vectors and CNNs: Challenges and Optimisations for the 2017 DCASE ASC Task"
: Proceedings of DCASE 2017, 2017
Original Titel:
Classifying Short Acoustic Scenes with I-Vectors and CNNs: Challenges and Optimisations for the 2017 DCASE ASC Task
Sprache des Titels:
Englisch
Original Buchtitel:
Proceedings of DCASE 2017
Original Kurzfassung:
This report describes the CP-JKU team?s submissions for Task 1
(Acoustic Scene Classification, ASC) of the DCASE-2017 challenge,
and discusses some observations we made about the data and
the classification setup. Our approach is based on the methodology
that achieved ranks 1 and 2 in the 2016 ASC challenge: a fusion of
i-vector modelling using MFCC features derived from left and right
audio channels, and deep convolutional neural networks (CNNs)
trained on raw spectrograms. The data provided for the 2017 ASC
task presented some new challenges ? in particular, audio stimuli
of very short duration. These will be discussed in detail, and our
measures for addressing them will be described. The result of our
experiments is a classification system that achieves classification
accuracies of around 90% on the provided development data, as estimated
via the prescribed four-fold cross-validation scheme. On
the unseen evaluation data, our best performing method achieved
73.8% and 5th place in the team ranking.
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