Our new article has been published on Scientific Reports! In this article, we introduce a novel machine learning tool, the periodicity coded nonnegative matrix factorization (PC-NMF). The PC-NMF can separate biological sounds from a noisy long-term spectrogram in an unsupervised approach, therefore, it is a great tool for evaluating the dynamics of soundscape and facilitating the soundscape-based biodiversity assessment.
You can download the MATLAB codes of PC-NMF and test data in the supplementary dataset of our article.
Improving biodiversity assessment via unsupervised separation of biological sounds from long-duration recordings
Scientific Reports 7, 4547 (2017) doi:10.1038/s41598-017-04790-7
Tzu-Hao Lin, Yu Tsao
Research Center for Information Technology Innovation, Academia Sinica, Taipei, Taiwan (R.O.C.)
Department of Electrical Engineering, Yuan Ze University, Taoyuan, Taiwan (R.O.C.)
Investigating the dynamics of biodiversity via passive acoustic monitoring is a challenging task, owing to the difficulty of identifying different animal vocalizations. Several indices have been proposed to measure acoustic complexity and to predict biodiversity. Although these indices perform well under low-noise conditions, they may be biased when environmental and anthropogenic noises are involved. In this paper, we propose a periodicity coded non-negative matrix factorization (PC-NMF) for separating different sound sources from a spectrogram of long-term recordings. The PC-NMF first decomposes a spectrogram into two matrices: spectral basis matrix and encoding matrix. Next, on the basis of the periodicity of the encoding information, the spectral bases belonging to the same source are grouped together. Finally, distinct sources are reconstructed on the basis of the cluster of the basis matrix and the corresponding encoding information, and the noise components are then removed to facilitate more accurate monitoring of biological sounds. Our results show that the PC-NMF precisely enhances biological choruses, effectively suppressing environmental and anthropogenic noises in marine and terrestrial recordings without a need for training data. The results may improve behaviour assessment of calling animals and facilitate the investigation of the interactions between different sound sources within an ecosystem.
2017/3/5-10 @ Academia Sinica, Taipei, Taiwan
Listening to the ecosystem: the integration of machine learning and a long-term soundscape monitoring network
Tzu-Hao Lin, Yu Tsao
Research Center for Information Technology Innovation, Academia Sinica
Taiwan Biodiversity Information Facility, Biodiversity Research Center, Academia Sinica
Academia Sinica Grid Computing
Information on the variability of environment and biodiversity is essential for conservation management. In recent years, soundscape monitoring has been proposed as a new approach to assess the dynamics of biodiversity. Soundscape is the collection of biological sound, environmental sound, and anthropogenic noise, which provide us the essential information regarding the nature environment, behavior of calling animals, and human activities. The recent developments of recording networks facilitate the field surveys in remote forests and deep marine environments. However, analysis of big acoustic data is still a challenging task due to the lack of sufficient database to recognize various animal vocalizations. Therefore, we have developed three tools for analyzing and visualizing soundscape data: (1) long-term spectrogram viewer, (2) biological chorus detector, (3) soundscape event classifier. The long-term spectrogram viewer helps users to visualize weeks or months of recordings and evaluate the dynamics of soundscape. The biological chorus detector can automatically recognize the biological chorus without any sound template. We can separate the biological chorus and non-biological noise from a long-term spectrogram and unsupervised identify various biological events by using the soundscape event classifier. We have applied these tools on terrestrial and marine recordings collected in Taiwan to investigate the variability of environment and biodiversity. In the future, we will integrate these tools with the Asian Soundscape monitoring network. Through the open data of soundscape, we hope to provide ecological researcher and citizens an interactive platform to study the dynamics of ecosystem and the interactions among acoustic environment, biodiversity, and human activities.