Recently, the research group of Professor Zhang Junguo of the College of Technology of BFU published the latest research on bird sound source separation “Automatic Bird Sound Source Separation based on Passive Acoustic Devices in Wild Environment ” in IEEE Internet of Things Journal (IF=10.6), a top journal in the field of Internet of Things.

The Internet of Things (IoT)-based passive acoustic monitoring (PAM) has shown great potential in large-scale remote bird monitoring. However, field recordings often contain overlapping signals, making precise bird information extraction challenging. To solve this challenge, first, the inter-channel spatial feature is chosen as complementary information to the spectral feature to obtain additional spatial correlations between the sources. Then, an end-to-end model named BACPPNet is built based on Deeplabv3plus and enhanced with the polarized self-attention mechanism to estimate the spectral amplitude mask (SMM) for separating bird vocalizations. Finally, the separated bird vocalizations are recovered from SMMs and the spectrogram of mixed audio using the inverse short Fourier transform (ISTFT). The research team evaluate the proposed method utilizing the generated mixed dataset. Experiments have shown that our method can separate bird vocalizations from mixed audio with RMSE, SDR, SIR, SAR, and STOI values of 2.82, 10.00dB, 29.90 dB, 11.08 dB, and 0.66, respectively, which are better than existing methods. Furthermore, the average classification accuracy of the separated bird vocalizations drops the least. This indicates that our method outperforms other compared separation methods in bird sound separation and preserves the fidelity of the separated sound sources, which might help us better understand wild bird sound recordings.

This research reveiced financial supports from the National Natural Science Foundation Project (62303063) and other projects. Associate Professor Xie Jiangjian and graduate students Shi Yuwei and Ni Dongming are the co-first authors of the paper. Professor Zhang Junguo from BFU and Professor Qian Kun from Beijing Institute of Technology are the co-corresponding authors, and Beijing Forestry University is the signature unit of the first author.

Paper link: https://ieeexplore.ieee.org/document/10399956