The problem of binary classification of acoustic signals of biological origin recorded in the real world is considered. Information features such as entropy and statistical complexity are chosen as the characteristic description of objects. The solution methods are based on three neural network architectures modified by the authors (on the Inception core, on the Inception core with the Residual technology, and on the Self-Attention structure with LSTM blocks). A dataset from the Kaggle competition for detecting acoustic signatures of whales was used, and the models were compared in terms of the quality of solving the problem involved on a standard set of metrics. The AUC ROC value of more than 90% was obtained, which means that the problem of detecting a useful signal is solved successfully and indicates that information features can be applied to similar tasks.