Cqt-based Convolutional Neural Networks for Audio Scene Classification

In this paper, we propose a parallel Convolutional Neural Network architecture for the task of classifying acoustic scenes and urban sound scapes. A popular choice for input to a Convolutional Neural Network in audio classification problems are Mel-transformed spectrograms. We, however, show in this paper that a ConstantQ-transformed input improves results. Furthermore, we evaluated critical parameters such as the number of necessary bands and filter sizes in a Convolutional Neural Network. These are non-trivial in audio tasks due to the different semantics of the two axes of the input data: time vs. frequency. Finally, we propose a parallel (graphbased) neural network architecture which captures relevant audio characteristics both in time and in frequency. Our approach shows a 10.7 % relative improvement of the baseline system of the DCASE 2016 Acoustic Scenes Classification task [1].