A Methodological Study of 1D CNN Classification of Marine Mammal Vocalizations with Variable Signal Durations

Marine mammal sound classification plays an important role in understanding species behavior, communication, and ecology. Automated classification methods have received increasing attention due to their ability to efficiently process and analyze large volumes of acoustic data. Traditional classification approaches often rely on frequency-domain representations, such as spectrograms, and image-based classifiers, which can be highly influenced by user-defined parameters. In this study, we investigate a classification method for marine mammal vocalizations using a one-dimensional convolutional neural network (1D CNN) that directly processes raw audio signals. The approach can handle signals of varying durations through a random cropping technique, minimizing signal distortion that is commonly introduced by conventional methods. The model was evaluated using marine mammal vocalization recordings obtained from the Watkins Marine Mammal Sound Database under three experimental scenarios. The results demonstrate the feasibility of using raw audio inputs with a 1D CNN for classifying marine mammal vocalizations with variable signal durations.