# Building Ensemble of Resnet for Dolphin Whistle Detection
## 1 Introduction
More recently, researchers have employed deep learning methods to detect whistle vocalizations. Deep neural networks have demonstrated great potential in general sound detection [23] and specific underwater acoustic monitoring [24]. The Convolutional Neural Network (CNN) is one of the best-known deep learners. Though commonly considered an image classifier, CNNs have been applied to whale vocalizations, significantly reducing the false-positive rates compared to traditional algorithms, while at the same time enhancing call detection [25; 26]. In [27], the authors compared four traditional methods for detecting dolphin echolocation clicks with six CNN architectures, demonstrating the superiority of the CNNs. In [28], CNNs were shown to outperform human experts in dolphin call detection accuracy. CNNs have also been applied to automatically categorize dolphin whistles into distinct groups, as in [29], and to extract whistle contours either by leveraging peak tracking algorithms [30] or by training CNN-based models for semantic segmentation [31]. Several studies of dolphin whistle classification have used data augmentation on the training set to enhance the performance of CNNs by reducing overfitting and increasing the size and variability of the available datasets [29; 30; 32]. Dolphin vocalizations are complex and highly variable, as analyzed in [33]. Unsurprisingly, some traditional music data augmentation methods, such as pitch shifting, time stretching, and adding background noise, have proven effective at this classification task. When synthesizing dolphin calls, care should be taken to apply augmentations to the audio signal rather than to the spectrograms, since altering the spectrogram could distort the time-frequency patterns of dolphin whistles, which would result in the semantic integrity of the labels being compromised [29; 34]. In [29], primitive shapes were interjected into the audio signal to generate realistic ambient sounds in negative samples, and classical computer vision methods were used to create synthetic time-frequency whistles, which replaced the training data. Generative Adversarial Networks (GANs) have also been employed to generate synthetic dolphin vocalizations [32]. This research underscores the efficacy of data augmentation and synthesis methods in enhancing both the precision and stability of dolphin whistle categorization models, especially in situations where the datasets are restricted or imbalanced. The goal of this work is to continue exploring data augmentation techniques for the task of dolphin vocalization detection. To this end, we use the benchmark dolphin whistles dataset developed by [PERSON] et al. [28], but apply data augmentation to the original test set of spectrograms to enlarge it rather than the training set. The training set contains all the spectrograms obtained from audio files recorded between 24 June and 30 June, while the test set is composed of the spectrograms of audio files recorded between 13 July and 15 July, a three-day window. Aside from augmenting the test set, we extract a three-day window (24-26 June) from the training set as the validation set. The proposed system outperforms previous state-of-the-art methods on the same dataset using the same testing protocol. We find our results interesting, especially since many misclassified audio samples are unclassifiable, even by humans. Therefore, the classification result of our method is likely very close to maximum performance (AUC = 1 is not obtainable).