# An Ensemble of Convolutional Neural Networks for Audio Classification
## 1 Introduction  
Sound classification and recognition have long been included in the field of pattern recognition. Some of the more popular application domains include speech recognition [1], music classification [2], biometric identification [3], and environmental sound recognition [4], the topic of interest in this work. Following the three classical pattern recognition steps of (i) preprocessing, (ii) feature/descriptor extraction, and (iii) classification, most early work in sound classification began by extracting features from audio recordings such as the Statistical Spectrum Descriptor or Rhythm Histogram [5]. Once it was recognized, however, that visual representations of audio, such as spectrograms [6] and Mel-frequency Cepstral Coefficients spectrograms (Mel) [7], contain valuable information, powerful texture extraction techniques like local binary patterns (LBP) [8] and its many variants [9] began to be explored for audio classification [2, 10]. In [2], for example, ensembles of classifiers designed to fuse sets of the most robust texture descriptors with acoustic features extracted from the audio traces on multiple datasets were exhaustively investigated; this study showed that the accuracy of systems based solely on acoustic or visual features could be enhanced by combining texture features. Recently, deep learners have proven even more robust in image recognition and classification than have texture analysis techniques. In audio biodiversity assessment, for example, a task that intends to monitor animal species at risk, convolution neural networks (CNNs) have greatly enhanced the performance of animal [11] and bird identification [12; 13; 14]. Deep learners have also been adapted to identify the sounds of marine animals [11] and fish [15]. In both these works, the authors combined CNN with visual features; the fusion of CNNs with traditional techniques was shown to outperform both the stand-alone conventional and single deep learning approaches. Another environmental audio recognition problem that is growing in relevancy has to do with identifying sources of noise in environments. This audio classification problem is of particular concern for cell phone developers since noise interferes with conversation. Consequently, datasets of extraneous sounds have been released to develop systems for handling different kinds of noise. The Environmental Sound Classification (ESC-50) dataset, for instance, contains 2000 labeled samples divided into fifty classes of environmental sounds that range from dogs barking to the sound of sea waves and chainsaws. In [16], a deep CNN achieved results superior to human classification on this dataset. Other work of interest in this area includes [17; 18; 19; 20; 21]. For all its power, deep learning also has significant drawbacks when it comes to environmental sound classification. For one, deep learning approaches require massive training data [22]. For audio classification, this means collecting large numbers of labeled audio signals and transforming them into visual representations, a task that is prohibitively expensive and labor-intensive. Fortunately, there are methods for increasing the number of images in small datasets. One such method is to apply data augmentation techniques. Audio signals can be augmented in both the time and frequency domains, and these augmentation techniques can be directly applied either on the raw signals themselves or on the images obtained after they have been converted into spectrograms. In [23], for example, several augmentation techniques were applied to the training set in the BirdCLEF 2018 dataset. The augmentation pipeline involved taking the original bird audio signals, chunking them, and then augmenting them in the time domain (e.g., by adding background/atmospheric noise) and in the frequency domain (e.g., by applying pitch shifts and frequency stretches). This augmentation process not only enlarged the dataset but also produced nearly a 10% improvement in identification performance.