LiTasNeT: A Bird Sound Separation Algorithm Based on Deep Learning

Recent advances in deep learning techniques and acoustic sensor networks offer a new way for continuously monitoring birds. Deep learning methods have led to considerable progresses in audio source separation (ASS). However, it is still a challenge to deploy models based on deep learning on embedded devices. Therefore, find an efficient solution to compact these large models without affecting ASS performance has become an important research topic. In birds' natural habitat, it is common for several birds to sing simultaneously. This phenomenon will lead to false results when identifying a particular bird species. Separate required bird sound from the recorded mixture becomes indispensable. In this paper, a novel so-called Lite TasNet (LiTasNeT) is proposed. Based on conventional ASS methods, LiTasNeT has obtained leading results in several standardized ASS areas. LiTasNeT is designed with parameter-sharing scheme to lower the memory consumption. Moreover, his low latency natures make it definitely suitable for real-time on-device applications.