# Capturing Discriminative Information Using a Deep Architecture in Acoustic Scene Classification
###### Abstract  
Acoustic scene classification contains frequently misclassified pairs of classes that share many common acoustic properties. Specific details can provide vital clues for distinguishing such pairs of classes. However, these details are generally not noticeable and are hard to generalize for different data distributions. In this study, we investigate various methods for capturing discriminative information and simultaneously improve the generalization ability. We adopt a max feature map method that replaces conventional non-linear activation functions in deep neural networks; therefore, we apply an element-wise comparison between the different filters of a convolution layer's output. Two data augmentation methods and two deep architecture modules are further explored to reduce overfitting and sustain the system's discriminative power. Various experiments are conducted using the \"detection and classification of acoustic scenes and events 2020 taskl-a\" dataset to validate the proposed methods. Our results show that the proposed system consistently outperforms the baseline, where the proposed system demonstrates an accuracy of 70.4% compared to the baseline at 65.1%.  
acoustic scene classification; light convolutional neural networks; deep neural networks +
Footnote †: journal: Applied Sciences  
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form noticeable clusters. However, we find that some scenes (e.g., airport and street_pedestrian) are widely scattered and thus evoke misclassification as illustrated in Figure 1b.  
In this study, we explore several methods for classifying noisy but informative signals, which are crucial for avoiding class confusion and improving the generalization ability. First, we utilize a light convolutional neural network (LCNN) architecture [15] rather than a common CNN. The LCNN adopts a max feature map (MFM) operation instead of a non-linear activation function such as ReLU or tanh. The LCNN demonstrates state-of-the-art performance when spoofing detection for automatic speaker verification (i.e., audio spoofing detection) [16, 17]. Second, data augmentation and attention-based deep architectural modules are explored to mitigate overfitting. Two data augmentation techniques, mix-up, and specAugment have also been investigated [18, 19]. The convolutional block attention module (CBAM) and squeeze and excitation (SE) networks are additionally exploited to enhance the discriminative power while using a few additional parameters [20, 21]. The main contributions of our work are:  
* We use an element-wise comparison between different filters of a convolution layer's output as the non-linear activation function, to emphasize the specific details of features to improve the performance on frequently misclassified pairs of classes that share common acoustic properties.
* We investigated two data augmentation methods, mix-up and specAugment, and two deep architecture modules, convolutional block attention module (CBAM) and squeeze and excitation (SE) networks, to reduce overfitting and sustain the system's discriminative power for the most confused classes.  
This paper is organized as follows. In Section 2, we briefly summarize the characteristics of ASC that motivate our works. Section 3 describes the proposed methods. Sections 4 and 5 present experimental details and results. Finally, we provide conclusions in Section 6.