# Bioacoustic Classification of Antillean Manatee  
Vocalization Spectrograms Using Deep Convolutional Neural Networks  
[PERSON]  
1 Grupo de Investigacion en Sistemas de Comunicaciones Digitales Avanzados (GISCDA), Facultad de Ingenieria de Electrica, Universidad Tecnologica de Panama, El Dorado, Panama City P.O. Box 0819-07289, Panama; [EMAIL_ADDRESS] (F.M. ); [EMAIL_ADDRESS] (A.G.); [EMAIL_ADDRESS] (H.P)  
[PERSON]  
1 Grupo de Investigacion en Sistemas de Comunicaciones Digitales Avanzados (GISCDA), Facultad de Ingenieria de Electrica, Universidad Tecnologica de Panama, El Dorado, Panama City P.O. Box 0819-07289, Panama; [EMAIL_ADDRESS] (F.M. ); [EMAIL_ADDRESS] (A.G.); [EMAIL_ADDRESS] (H.P)  
[PERSON]  
1 Grupo de Investigacion en Sistemas de Comunicaciones Digitales Avanzados (GISCDA), Facultad de Ingenieria de Electrica, Universidad Tecnologica de Panama, El Dorado, Panama City P.O. Box 0819-07289, Panama; [EMAIL_ADDRESS] (F.M. ); [EMAIL_ADDRESS] (A.G.); [EMAIL_ADDRESS] (H.P)  
[PERSON]  
3 Smithsonian Tropical Research Institute, Panama City P.O. Box 0843-03092, Panama; [EMAIL_ADDRESS]  
[PERSON]  
24 Grupo de Investigaciones en Biotecnologia, Bioinformatica y Biologia de Sistemas (GIBBS), Facultad de Ingenieria de Sistemas Computacionales, Universidad Tecnologica de Panama, El Dorado, Panama City P.  
For the manatee populations in Bocas del Toro Province, Panama, this task is intensely challenging since the rivers present turbid brackish waters covered by aquatic vegetation common in tropical wetlands. Hence, traditional visual sightings and aerial surveys are not reliable [3]. In Panamanian wetlands, aerial and sonar surveys were previously used to estimate manatee populations [2; 4]. However, these two approaches present logistical, performance and cost challenges to systematically estimate and to monitor manatee population changes. In this context, the authors proposed a reliable scheme to identify and count manatee using underwater passive recordings to support other fragmentary efforts to estimate the Antillean manatee population in Panamanian wetlands [5], considerably improving previous population estimates described in [3]. This scheme takes advantage of the features of the bioacoustic sounds (vocalizations) produced by manatees that were previously described in [6; 7]. The scheme consisted of four stages including: detection, denoising, signal classification and individual counting and identification by vocalization clustering. This methodology was applied to analyze around 450,000 (2-min) audio clips continuously recorded for a period of three years, from April 2015 to May 2018, at four permanent monitoring sites in the Changuinola and San San rivers in Bocas del Toro, western Caribbean Panama. The vocalization detection, denoising and classification stages were based on signal processing methods. The detection stage consisted on the analysis of the autocorrelation function in the wavelet domain. For the denoising stage, a signal subspace approach was implemented [8; 9]. With the previously detected and denoised signals, the classification stage consisted of a modified version of the harmonic method proposed by [PERSON] et al. [10] that included the search of harmonic components in the fast Fourier transform (FFT) spectrum of the signals. Using this approach, the combination of the detection and classification stages provided relatively low global true positive rates (also known as sensibility or recall). Indeed, the detection stage presented recall values in the range of 0.63 to 0.74 for Changuinola River and 0.47 to 0.55 for San San River. From those detected signals, the classification stage presented recall values of 0.96 to 0.59 in Changuinola River and 0.90 to 0.55 for San San River.