Marine mammal sound classification based on a parallel recognition model and octave analysis

The ocean is full of a variety of sounds from natural, biological or anthropogenic sources. Listening to the animals sounds allows scientists to detect, identify, and locate different endangered species as well as listening to high intensity anthropogenic sources, which could harm the marine ecosystem. In this work, a new computational model for marine mammals classification is presented and validated with data from an online database. The feature extraction is performed using 1/6 octave analysis and the classification is carried out based on an independent ensemble methodology, where the outputs of four parallel feed forward neural networks are combined to classify eleven possible classes (seven marine mammals plus four additional classes). Unlike similar works, this paper considers multiple sounds emitted by each species such as whistles, calls and squeaks. The model demonstrated favorable performance reaching a classification rate of 90% at a low computational cost.