Deep Sequential Image Features for Acoustic Scene Classification

For the Acoustic Scene Classification task of the IEEE AASP Challenge on Detection and Classification of Acoustic Scenes and Events (DCASE2017), we propose a novel method to classify 15 different acoustic scenes using deep sequential learning, based on features extracted from Short-Time Fourier Transform and scalogram of the audio scenes using Convolutional Neural Networks. It is the first time to investigate the performance of bump and morse scalograms for acoustic scene classification in an according context. First, segmented audio waves are transformed into a spectrogram and two types of scalograms; then, ‘deep features’ are extracted from these using the pre-trained VGG16 model by probing at the fully connected layer. These representations are then fed into Gated Recurrent Neural Networks for classification separately. Predictions from the three systems are finally combined by a margin sampling value strategy. On the official development set of the challenge, the best accuracy on a four-fold cross-validation setup is 80.9%, which increases by 6.1% when compared with the official baseline (p < .001 by one-tailed z-test).