# Animal Sound Classification Using Dissimilarity Spaces
## 1 Introduction  
Over the past decade, research in sound classification and recognition has gained in popularity and rapidly broaden in its application from the more traditional focus on speech recognition [1] and music genre classification [2] to biometric identification [3], computer-aided heart sound detection [4], environmental audio scene and sound recognition [5; 6], biodiversity assessment [7], human voice classification and emotion recognition [8], English accent classification, and gender identification [9], to list a few of a wide range of application areas. As with research in pattern recognition generally, the features fed into classifiers were initially engineered, which, in the case of sound applications, meant extracting from raw audio traces such descriptors as the Statistical Spectrum Descriptor and Rhythm Histogram [10]. In the past decade, researchers began exploring the possibility of visually representing audio signals to apply more powerful image classification descriptors and techniques. Initially, visual representations of audio traces centered around the display overtime of the frequency spectrum: examples of these representations include the spectrogram [11] and Mel-frequency Cepstral Coefficients spectrogram [12]. Although a spectrogram is typically a graph with two dimensions (time and frequency), additional dimensions can be included, such as pixel intensity [13], which includes at each time step the representation of an audio signal's amplitude in a specific frequency. Initially, popular texture descriptors, such as Haralick's Grey Level Co-occurrence Matrices (GLCMs) [14], Gabor filters [15], and Local Binary Patterns (LBP) [16] and its variants [17] were extracted from these spectrograms for the task of music genre classification [18; 19; 20]. Fusions of a large set of state-of-the-art texture descriptors were then experimentally applied to spectrograms, and certain combinations were shown to enhance further the accuracy of music genre classification [2]. The rise in popularity of deep learning due to affordable graphic processing units (GPUs) changed the trajectory in machine learning research [21]. Deep learners, such as the convolutional neural network (CNN), produced far superior results to most other classifiers when it came to image classification [22]. Consequently, more attention was placed on representing acoustic traces through visual representations so that deep learning approaches could be applied. Engineered features diminished in importance since deep classifiers learn which patterns perform best for a specific problem during the training process. However, engineered features have been shown to augment deep learning approaches when fused. Early work with CNNs applied to visual representations obtained state-of-the-art in chord detection and recognition [23; 24] and in music genre classification [25]. In [25], for example, spectrograms were converted into GCLM maps and trained on CNNs. In [26], canonical approaches, such as LBP-trained SVMs were fused with CNNs and shown to outperform previous systems. Another development in sound classification involves the design of deep learners and feature sets specific to audio classification. For example, in [27], the authors explored variations in CNN architecture and parameters; in [28] a novel _sparse coding_ CNN was developed and shown to perform well if not better than the state-of-the-art for sound event recognition and retrieval. Also of note is the hybrid multimodal deep learning approach proposed in [29] for multilabel music genre classification that combined album cover images, reviews, and audio tracks; this system was shown to outperform the single-modality approaches. In [30] the authors present a gated CNN and a temporary attention-based classification system for audio classification from weakly labeled data.