# Spectrogram Classification Using Dissimilarity Space
## 1 Introduction  
Sound classification and recognition have been applied in different domains, e.g., speech recognition [1], music classification [2], environmental sound recognition, and biometric identification [3]. Traditionally, in pattern recognition problems, features have been extracted from the actual audio traces (e.g., Statistical Spectrum Descriptor and Rhythm Histogram [4]). However, by replacing audio traces by their visual representation, image classification techniques can be used to extract features on sound classification problems. The most commonly used visual representation of audio traces involves the display of their frequency spectrum as they vary in time, as in spectrograms [5] and Mel-frequency Cepstral Coefficients spectrograms [6]. A spectrogram can be described as a graph with two dimensions (time and frequency) plus a third dimension in terms of pixel intensity [7] that represents the signal amplitude in a specific frequency at a particular time step. [PERSON] et al. [8, 9] applied several classification and texture analysis techniques to music genre classification using such a method. In [9], the authors extracted grey level co-occurrence matrices (GLCMs) [10] from spectrograms, while in [8] they used the local binary pattern (LBP) [11], which is a popular texture descriptor. In [12], two other feature descriptors were extracted from audio images: local phase quantization (LPQ) and Gabor filters [13]. In 2017, [PERSON] et al. [2] demonstrated on multiple audio datasets how the fusion of acoustic features extracted from audio traces using state-of-the-art texture descriptors greatly improves the accuracy of acoustic and visual feature-based systems. When deep learning became popular and Graphic Processing Units (GPUs) became more powerful at accessible costs, traditional pattern recognition changed, and attention focused evenmore on visual representations of acoustic traces. In the traditional machine learning framework, the optimization of the feature extraction step plays a key role, especially with the evolution of handcrafted features, which minimize the distance between patterns of the same class in the feature space while simultaneously attempting to maximize their distance from the patterns of other classes. Since deep classifiers learn the best features for describing patterns during the training process, these engineered features have diminished in significance, playing in the deep framework more of a supporting role when combined with features extracted from visual representations of acoustic traces that the deep classifiers determine are most informative. Another reason for the growing popularity of representing audio as images is the fact that the convolutional neural network (CNN), one of the most famous deep classifiers, requires images for its input. In their study, [PERSON] and [PERSON] [14, 15] explored CNNs as an alternative approach to music classification problems, establishing the state-of-the-art in automatic chord detection and recognition. [PERSON] et al. [16] converted spectrograms into GCLM maps to train CNNs for music genre classification, and [PERSON] et al. [17] performed better than the state-of-the-art on the LMD dataset by fusing canonical approaches, e.g., LMP-trained SVMs with CNNs. Only a few studies, however, have focused on making these processes that were designed for image classification more specific for sound image recognition. In their study, [PERSON] and [PERSON] [18] focused on adjusting CNN parameters and structures and showed how using Rectified Linear Units (ReLu) instead of stochastic gradient descent with the Hessian Free optimization and sigmoid units reduced training time. [PERSON] et al. [19] presented an innovative CNN, which they named a _sparse coding CNN_, for sound event recognition and retrieval, which, when evaluated under noisy and clean conditions, achieved competitive and sometimes better performance than the majority of other approaches.