# Building Ensemble of Resnet for Dolphin Whistle Detection
## 1 Introduction  
Marine ecosystems play a critical role in maintaining the balance of our planet's ecosystem by supporting food security and contributing to climate regulation [1], making their preservation essential for the long-term sustainability of the earth's environment. Thus, there is a growing need to develop and test innovative monitoring systems to ensure the natural preservation of marine habitats. Modern technologies have already shown great potential in monitoring habitats and advancing our understanding of marine communities [2]. Acoustic methods are commonly used for underwater investigations because they can detect and classify sensitive targets, even in low visibility conditions. Passive acoustic technologies (PAM), such as underwater microphones, or hydrophones, are particularly attractive, as they allow for non-invasive continuous monitoring of marine ecosystems without interfering with biological processes [3]. PAM has been shown to achieve various research and management goals by effectively detecting animal calls [4]. These objectives may include tracking and localizing animals [5, 6], species identification, identifying individuals [3, 7], analyzing distributions and behavior [8], and estimating animal density [9]. The bottlenose dolphin (Tursiops truncatus) is a highly intelligent marine mammal and a critical species for researchers studying marine ecosystems [10]. Like many other marine mammals, dolphins are acoustic specialists that rely on sounds for communication, reproduction, foraging, and navigational purposes. The acoustic communication of dolphins employs a wide range of vocalizations, including clicks, burst-pulses, buzzes, and whistles [11]. Whistles, in particular, serve various social functions such as individual identification, group cohesion, and coordination of activities, such as feeding, resting,socializing, and navigation [12]. Understanding and accurately detecting dolphin vocalizations is essential for monitoring their populations and assessing their role within marine ecosystems. Traditional bioacoustics tools and algorithms for detecting dolphins have relied on spectrogram analysis, manual signal processing, and statistical methods [13]. For example, the reference approach pursued in [14] applies three noise removal algorithms to the spectrogram of a sound sample. Then, a connected region search is conducted to link together sections of the spectrogram that are above a predetermined threshold and close in time and frequency. A similar technique exploits a probabilistic Hough transform algorithm to detect ridges similar to thick line segments, which are then adjusted to the geometry of the potential whistles in the image via an active contour algorithm [15]. Other algorithmic methods aim to quantify the variation in complexity (randomness) occurring in the acoustic time series containing the vocalization; for example, by measuring signal entropy [16]. While these techniques have helped the study of dolphin vocalizations, they can be time-consuming and may not always provide accurate results due to the complexity and variability of the signals. Researchers have thus turned to machine learning methods to improve detection accuracy and efficiency. Early machine learning studies in the field of dolphin detection applied traditional classifiers, such as Hidden Markov Models (HMM) [17] and Support Vector Machines (SVMs) [18]. For instance, in [19], a hidden Markov model was utilized for whistle classification, and in [20], classification and regression tree analysis was employed along with discriminant function analysis for categorizing parameters extracted from whistles. In [21], a multilayer perceptron classifier was implemented for classifying short-time Fourier transforms (STFTs) and wavelet transform coefficient energies of whistles. Lastly, in [15] a random forest algorithm and a support vector machine were combined to classify features derived from the duration, frequency, and cepstrum domain of whistles (see [22] for a review of the early literature).