Please use this identifier to cite or link to this item: https://doi.org/10.1145/3240508.3240631
Title: Learning and fusing multimodal deep features for acoustic scene categorization
Authors: Yin, Y 
Shah, RR
Zimmermann, R
Issue Date: 15-Oct-2018
Publisher: ACM
Citation: Yin, Y, Shah, RR, Zimmermann, R (2018-10-15). Learning and fusing multimodal deep features for acoustic scene categorization. MM '18: ACM Multimedia Conference : 1892-1900. ScholarBank@NUS Repository. https://doi.org/10.1145/3240508.3240631
Abstract: Convolutional Neural Networks (CNNs) have been widely applied to audio classification recently where promising results have been obtained. Previous CNN-based systems mostly learn from two-dimensional time-frequency representations such as MFCC and spectrograms, which may tend to emphasize more on the background noise of the scene. To learn the key acoustic events, we introduce a three-dimensional CNN to emphasize on the different spectral characteristics from neighboring regions in spatial-temporal domain. A novel acoustic scene classification system based on multimodal deep feature fusion is proposed in this paper, where three CNNs have been presented to perform 1D raw waveform modeling, 2D time-frequency image modeling, and 3D spatial-temporal dynamics modeling, respectively. The learnt features are shown to be highly complementary to each other, which are next combined in a feature fusion network to obtain significantly improved classification predictions. Comprehensive experiments have been conducted on two large-scale acoustic scene datasets, namely the DCASE16 dataset and the LITIS Rouen dataset. Experimental results demonstrate the effectiveness of our proposed approach, as our solution achieves state-of-the-art classification rates and improves the average classification accuracy by 1.5% ∼ 8.2% compared to the top ranked systems in the DCASE16 challenge.
Source Title: MM '18: ACM Multimedia Conference
URI: https://scholarbank.nus.edu.sg/handle/10635/200727
ISBN: 9781450356657
DOI: 10.1145/3240508.3240631
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