On the performance of different excitation-residual blocks for Acoustic Scene Classification

Acoustic Scene Classification (ASC) is a problem related to the field of machine listening whose objective is to classify/tag an audio clip in a predefined label describing a scene location. Interest in this topic has grown so much over the years that an annual international challenge (Dectection and Classification of Acoustic Scenes and Events) is held to propose novel solutions. Solutions to these problems often incorporate different methods such as data augmentation or with an ensemble of various models. Although the main line of research in the state-of-the-art usually implements these methods, considerable improvements and state-of-the-art results can also be achieved only by modifying the architecture of convolutional neural networks (CNNs). In this work we propose two novel squeeze-excitation blocks to improve the accuracy of an ASC framework by modifying the architecture of the residual block in a CNN together with an analysis of several state-of-the-art blocks. The main idea of squeeze-excitation blocks is to learn spatial and channel-wise feature maps independently instead of jointly as standard CNNs do. This is done by some global grouping operators, linear operators and a final calibration between the input of the block and the relationships obtained by that block. The behavior of the block that implements these operators and, therefore, the entire neural network can be modified depending on the input to the block, the residual configurations and the non-linear activations, that is, at what point of the block they are performed. The analysis has been carried out using TAU Urban Acoustic Scenes 2019 dataset presented in DCASE 2019 edition. All configurations discussed in this document exceed baseline proposed by DCASE organization by 13% percentage points. In turn, the novel configurations proposed in this paper exceed the residual configuration proposed in previous works.