Pairwise Decomposition with Deep Neural Networks and Multiscale Kernel Subspace Learning for Acoustic Scene Classification

We propose a system for acoustic scene classification using pairwise decomposition with deep neural networks and dimensionality reduction by multiscale kernel subspace learning. It is our contribution to the Acoustic Scene Classification task of the IEEE AASP Challenge on Detection and Classification of Acoustic Scenes and Events (DCASE2016). The system classifies 15 different acoustic scenes. First, auditory spectral features are extracted and fed into 15 binary deep multilayer perceptron neural networks (MLP). MLP are trained with the `one-against-all' paradigm to perform a pairwise decomposition. In a second stage, a large number of spectral, cepstral, energy and voicing-related audio features are extracted. Multiscale Gaussian kernels are then used in constructing optimal linear combination of Gram matrices for multiple kernel subspace learning. The reduced feature set is fed into a nearest-neighbour classifier. Predictions from the two systems are then combined by a threshold-based decision function. On the official development set of the challenge, an accuracy of 81.4% is achieved.