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Wavelets Revisited for the Classification of Acoustic Scenes


We investigate the effectiveness of wavelet features for acoustic scene classification as contribution to the subtask of the IEEE AASP Challenge on Detection and Classification of Acoustic Scenes and Events (DCASE2017). On the back-end side, gated recurrent neural networks (GRNNs) are compared against traditional support vector machines (SVMs). We observe that, the proposed wavelet features behave comparable to the typically-used temporal and spectral features in the classification of acoustic scenes. Further, a late fusion of trained models with wavelets and typical acoustic features reach the best averaged 4-fold cross validation accuracy of 83.2%, and 82.6% by SVMs, and GRNNs, respectively; both significantly outperform the baseline (74.8%) of the official development set (p<0.001, one-tailed z-test).
Title: Wavelets Revisited for the Classification of Acoustic Scenes
Lecturer: Kun Qian
Date: 24-10-2017
Building/Room: Eichleitnerstraße 30 / 207
Contact: U Augsburg/TUM