Two presentations at the 10th International Conference on Ecological Informatics!

Information retrieval from marine soundscape by using machine learning-based source separation

Tzu-Hao Lin 1, Tomonari Akamatsu 2, Yu Tsao 3, Katsunori Fujikura1

1 Department of Marine Biodiversity Research, Japan Agency for Marine-Earth Science and Technology, Japan
2 National Research Institute of Fisheries Science, Japan Fisheries Research and Education Agency, Japan
3 Research Center for Information Technology Innovation, Academia Sinica, Taiwan

In remote sensing of the marine ecosystem, visual information retrieval is limited by the low visibility in the ocean environment. Marine soundscape has been considered as an acoustic sensing platform of the marine ecosystem in recent years. By listening to environmental sounds, biological sounds, and human-made noises, it is possible to acoustically identify various geophysical events, soniferous marine animals, and anthropogenic activities. However, the sound detection and classification remain a challenging task due to the lack of underwater audio recognition database and the simultaneous interference of multiple sound sources. To facilitate the analysis of marine soundscape, we have employed information retrieval techniques based on non-negative matrix factorization (NMF) to separate different sound sources with unique spectral-temporal patterns in an unsupervised approach. NMF is a self-learning algorithm which decomposes an input matrix into a spectral feature matrix and a temporal encoding matrix. Therefore, we can stack two or more layers of NMF to learn the spectral-temporal modulation of k sound sources without any learning database [1]. In this presentation, we will demonstrate the application of NMF in the separation of simultaneous sound sources appeared on a long-term spectrogram. In shallow water soundscape, the relative change of fish chorus can be effectively quantified even in periods with strong mooring noise [2]. In deep-sea soundscape, cetacean vocalizations, an unknown biological chorus, environmental sounds, and systematic noises can be efficiently separated [3]. In addition, we can use the features learned in procedures of blind source separation as the prior information for supervised source separation. The self-adaptation mechanism during iterative learning can help search the similar sound source from other acoustic dataset contains unknown noise types. Our results suggest that the NMF-based source separation can facilitate the analysis of the soundscape variability and the establishment of audio recognition database. Therefore, it will be feasible to investigate the acoustic interactions among geophysical events, soniferous marine animals, and anthropogenic activities from long-duration underwater recordings.

Improving acoustic monitoring of biodiversity using deep learning-based source separation algorithms

Mao-Ning Tuanmu1, Tzu-Hao Lin2, Joe Chun-Chia Huang1, Yu Tsao3, Chia-Yun Lee1

1Biodiversity Research Center, Academia Sinica, Taiwan
2Department of Marine Biodiversity Research, Japan Agency for Marine-Earth Science and Technology, Japan
3Research Center for Information Technology Innovation, Academia Sinica, Taiwan

Passive acoustic monitoring of the environment has been suggested as an effective tool for investigating the dynamics of biodiversity across spatial and temporal scales. Recent development in automatic recorders has allowed environmental acoustic data to be collected in an unattended way for a long duration. However, one of the major challenges for acoustic monitoring is to identify sounds of target taxa in recordings which usually contain undesired signals from non-target sources. In addition, high variation in the characteristics of target sounds, co-occurrence of sounds from multiple target taxa, and a lack of reference data make it even more difficult to separate acoustic signals from different sources. To overcome this issue, we developed an unsupervised source separation algorithm based on a multi-layer (deep) non-negative matrix factorization (NMF). Using reference echolocation calls of 13 bat species, we evaluated the performance of the multi-layer NMF in separating species-specific calls. Results showed that the multi-layer NMF, especially when being pre-trained with reference calls, outperformed the conventional supervised single-layer NMF. We also evaluated the performance of the multi-layer NMF in identifying different types of bat calls in recordings collected in the field. We found comparable performance in call types identification between the multi-layer NMF and human observers. These results suggest that the proposed multi-layer NMF approach can be used to effectively separate acoustic signals of different taxa from long-duration field recordings in an unsupervised manner. The approach can thus improve the applicability of passive acoustic monitoring as a tool to investigate the responses of biodiversity to the changing environment.


Monitoring of coral reef ecosystem: an integrated approach of marine soundscape and machine learning

Presented in International Symposium on Grids & Clouds 2018

Monitoring of coral reef ecosystem: an integrated approach of marine soundscape and machine learning

 Tzu-Hao Lin1, Tomonari Akamatsu2, Frederic Sinniger3, Saki Harii3, Yu Tsao1

1Research Center for Information Technology Innovation, Academia Sinica, Taipei, Taiwan
2National Research Institute of Fisheries Science, Japan Fisheries Research and Education Agency, Yokohama, Japan
3Tropical Biosphere Research Center, University of the Ryukyus, Okinawa, Japan

Coral reefs represent the most biologically diverse marine ecosystem, however, they are vulnerable to environmental changes and impacts. Therefore, information on the variability of environment and biodiversity is essential for the conservation management of coral reefs. In this study, a soundscape monitoring network of shallow and mesophotic coral reefs was established in Okinawa, Japan. Three autonomous sound recorders were deployed in water depths of 1.5 m, 20 m, and 40 m since May 2017. To investigate the soundscape variability, we applied the periodicity-coded nonnegative matrix factorization to separate biological sounds and the other noise sources displayed on long-term spectrograms. The separation results indicate that the coral reef soundscape varied among different locations. At 1.5 m depth, biological sounds were dominated by snapping shrimp sounds and transient fish calls. Although not knowing the specific source, noises were clearly driven by tidal activities. At 20 m and 40 m depths, biological sounds were dominated by nighttime fish choruses and noises were primary related to shipping activities. Furthermore, the clustering result indicates the complexity of biological sounds was higher in mesophotic coral reefs compare to shallow-water coral reefs. Our study demonstrates that the integration of machine learning in the analysis of soundscape is efficient to interpret the variability of biological sounds, environmental and anthropogenic noises. Therefore, the conservation management of coral reefs, especially those rarely studied such as mesophotic coral reefs, can be facilitated by the long-term monitoring of coral reef soundscape.

You can also check the slides of this talk.

PNC 2017 Annual Conference and Joint Meetings

2017/11/7-9 @ Tainan, Taiwan

Computing biodiversity change via a soundscape monitoring network

Tzu-Hao Lin, Yu Tsao
Research Center for Information Technology Innovation, Academia Sinica

Yu-Huang Wang
Taiwan Academy of Ecology

Han-Wei Yen
Academia Sinica Grid Computing Centre

Sheng-Shan Lu
Taiwan Forestry Research Institute

A monitoring network for biodiversity change is essential for wildlife conservation. In recent years, many soundscape monitoring projects have been carried out to investigate the diversity of vocalizing animals. However, the acoustic-based biodiversity assessment remains challenging due to the lack of sufficient recognition database and the inability to disentangle mixed sound sources. Since 2014, an Asian Soundscape monitoring project has been initiated in Taiwan. So far, there are 15 recording sites in Taiwan and three sites in Southeast Asia, with more than 20,000 hours of recordings archived in the Asian Soundscape. In this study, we employed the visualization of long-duration recordings, blind source separation, and clustering techniques, to investigate the spatio-temporal variations of forest biodiversity in the Triangle Mountain, Lienhuachih, and Taipingshan. On the basis of blind source separation, biological sounds, with prominent diurnal occurrence pattern, can be separated from the environmental sounds without any recognition database. Thus, clusters of biological sounds can be effectively identified and employed to measure the daily change in bioacoustic diversity. Our results show that the bioacoustic diversity was higher in the evergreen broad-leaved forest. However, the seasonal variation in bioacoustic diversity was most evident in the high elevation coniferous forest. This study demonstrates that a suitable integration of machine learning and ecoacoustics can facilitate the evaluation of biodiversity changes. In addition to biological activities, we can also measure the environmental variability from soundscape information. In the future, the Asian Soundscape will not only serve as an open database for soundscape recordings, but also will provide tools for analyzing the interactions between biodiversity, environment, and human activities.

If you are interested in this research, please check the full paper published in PNC 2017.

5th Joint Meeting of the Acoustical Society of America and Acoustical Society of Japan

2016/11/28-12/2 @ Honolulu, USA

Acoustic response of Indo-Pacific humpback dolphins to the variability of marine soundscape

Tzu-Hao Lin, Yu Tsao
Research Center for Information Technology Innovation, Academia Sinica

Shih-Hau Fang
Department of Electrical Engineering, Yuan Ze University

Chih-Kai Yang, Lien-Siang Chou
Institute of Ecology and Evolutionary Biology, National Taiwan University

Marine mammals can adjust their vocal behaviors when they encounter anthropogenic noise. The acoustic divergence among different populations has also been considered as the effect of ambient noise. The recent studies discover that the marine soundscape is highly dynamic; however, it remains unclear how marine mammals alter their vocal behaviors under various acoustic environments. In this study, autonomous sound recorders were deployed in western Taiwan waters between 2012 and 2015. Soundscape scenes were unsupervised classified according to acoustic features measured in each 5 min interval. Non-negative matrix factorization was used to separate different scenes and to inverse the temporal occurrence of each soundscape scene. Echolocation clicks and whistles of Indo-Pacific humpback dolphins, which represent the only marine mammal species occurred in the study area, were automatically detected and analyzed. The preliminary result indicates the soundscape scenes dominated by biological sounds are correlated with the acoustic detection rate of humpback dolphins. Besides, the dolphin whistles are much complex when the prey associated scene is prominent in the local soundscape. In the future, the soundscape information may be used to predict the occurrence and habitat use of marine mammals.


2017/1/23-24 @ 高雄中山大學







近海與海岸環境 Land-Ocean Interactions in the Changing Coastal Zones of Taiwan:
Scientific Basis and Societal Engagements






可能很多人頭上會冒出很多問號,生態系的變化可以用眼睛觀察,但是要怎麼樣聽呢? 其實錄音也是一種不同於目視觀察的科學研究方法,透過收聽環境裡面的背景噪音(例如溪流、風聲和雨聲)、動物的叫聲(求偶聲、覓食聲)、以及人為活動的噪音(車聲、開發活動噪音)。最近一篇刊載Science的文章,就專文介紹了近年來科學界利用被動式聲學(passive acoustic monitoring)探討生物多樣性、生態系變動的研究方法。有興趣的人可以到下列網址閱讀全文,或在此觀看Science-2014-Servick-834-7

在每一個環境中所聽到的每一個聲音都有其來源(生物性 or 非生物性),透過追蹤這些不同聲音的來源,其實我們可以推敲某些動物是否在此活動,或是了解當時環境的狀況。舉例來說,早晨時聽到雞鳴,即使我們沒有看到任何東西,我們也自然的會聯想到附近有一隻公雞。當我們聽到窗外的暴雨聲,我們會跟同事哀怨著又下起了午後雷陣雨。這些推測並不是來自於親自看到,而是來自於聽到的體驗。這個簡單的概念現在被應用在野生動物研究上,當我們知道昆蟲、蛙類、鳥類、哺乳動物發出的各種聲音,我們便可以利用這些聲音的出現時間來推敲這些發聲動物的主要活動時間,甚至了解一整個群聚的生態變動。

例如,當我們去爬郊山時,傍晚走在溪澗、山溝附近時,經常可以聽到許多種類的蛙鳴。但是隨著山區道路的開發,山溝漸漸水泥化,慢慢地你可能還是會聽到很大量的蛙鳴,但是聲音的種類變少了。我想這樣的狀況,就連國小六年級的學生都想到的,這個生態系到底發生了甚麼改變吧? 環境中的生態多樣性可能隨著山溝環境的改變而降低了。

那可能有人會問,如果要了解生態多樣性的改變,我們不就找人去調查就好了嗎? 的確,要真正的證實環境變化,最好的方法就是直接了解這個生態系的物種組成。但是人難找、錢也沒有阿! 生態系調查其實不是我們想像的這麼簡單,派幾個人去做做紀錄、翻翻圖鑑就好。這些調查人員往往需要長時間的訓練,更需要對分類物種的技術熟悉,甚至可能還要時時對現有的物種分類依據抱持懷疑。假使人有了,錢呢? 不要說政府對於環境監控的冷漠,就連一般企業和民眾可能也覺得我幹嘛要注意我周遭環境的變化勒,那不是政府的責任嗎? 我想正是整個社會對於環境監測的冷漠,造就了很多開發過程的衝突。