Acoustic scene classification with fully convolutional neural networks and i-vectors

B. Lehner, H. Eghbal-zadeh, C. Heindl, F. Paischer, G. Widmer. Acoustic scene classification with fully convolutional neural networks and i-vectors. pages http://dcase.community/challenge2018/task-acoustic-scene-classi, 7, 2018.

Autoren
  • Bernhard Lehner
  • Hamid Eghbal-zadeh
  • Christoph Heindl
  • Fabian Paischer
  • Gerhard Widmer
TypSonstiges
Monat7
Jahr2018
Seitenhttp://dcase.community/challenge2018/task-acoustic-scene-classi
Abstract

This technical report describes the CP-JKU team’s submissions for Task 1 - Subtask A (Acoustic Scene Classification, ASC) of the DCASE-2018 challenge. Our approach is still related to 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 spectrograms. However, for our 2018 submission we have put a stronger focus on tuning and pushing the performance of our CNNs. The result of our experiments is a classification system that achieves classification accuracies of around 80% on the public Kaggle-Leaderboard.