Separating overlapping bat calls with a bi-directional long short-term memory network.

Acquiring clear acoustic signals is critical for the analysis of animal vocalizations. Bioacoustics studies commonly face the problem of overlapping signals, which can impede the structural identification of vocal units, but there is currently no satisfactory solution. This study presents a bi-directional long short-term memory network to separate overlapping echolocation-communication calls of 6 different bat species and reconstruct waveforms. The separation quality was evaluated using 7 temporal-spectrum parameters. All the echolocation pulses and syllables of communication calls in the overlapping signals were separated and parameter comparisons showed no significant difference and negligible deviation between the extracted and original calls. Clustering analysis was conducted with separated echolocation calls from each bat species to provide an example of practical application of the separated and reconstructed calls. The result of clustering analysis showed high corrected rand index (82.79%), suggesting the reconstructed waveforms could be reliably used for species classification. These results demonstrate a convenient and automated approach for separating overlapping calls. The study extends the application of deep neural networks to separate overlapping animal sounds.