Information retrieval from a soundscape by using blind source separation and clustering

Passive acoustic monitoring represents one of the remote sensing platforms of biodiversity. However, it remains challenging to retrieve meaningful biological information from a large amount of soundscapedata when a comprehensive recognition database is not available. To overcome this issue, it is necessary to investigate the basic structure of a soundscapeand subsequently retrieve biological information. The recent development of machine learning-based blind source separationtechniques allow us to separate biological choruses and non-biological sounds appearing on a long-term spectrogram. After the blind source separation, the temporal-spatial changes of bioacousticactivities can be efficiently investigated by using a clustering algorithm. In this presentation, we will demonstrate the information retrievalin the forest and marine soundscapes. The separation result shows that in addition to biological information, we can also extract information relevant to weather patterns and human activities. Furthermore, the clustering result can be used to establish an audio library of nature soundscapes, which may facilitate the investigation of interactions among wildlife, climate change, and human development. In the future, the soundscape-based ecosystem monitoring will be feasible if we can integrate the soundscape information retrievalin a large-scale soundscapemonitoring network.Passive acoustic monitoring represents one of the remote sensing platforms of biodiversity. However, it remains challenging to retrieve meaningful biological information from a large amount of soundscapedata when a comprehensive recognition database is not available. To overcome this issue, it is necessary to investigate the basic structure of a soundscapeand subsequently retrieve biological information. The recent development of machine learning-based blind source separationtechniques allow us to separate biological choruses and non-biological sounds appearing on a long-term spectrogram. After the blind source separation, the temporal-spatial changes of bioacousticactivities can be efficiently investigated by using a clustering algorithm. In this presentation, we will demonstrate the information retrievalin the forest and marine soundscapes. The separation result shows that in addition to biological information, we can also extract information relevant to weather patterns and human activities. Furth...