Improving the evaluation of soundscape variability via blind source separation

Presented in 174th Meeting of the Acoustical Society of America @ New Orleans, USA

Improving the evaluation of soundscape variability via blind source separation

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

Tomonari Akamatsu
National Research Institute of Fisheries Science, Japan Fisheries Research and Education Agency

Mao-Ning Tuanmu, Joe Chun-Chia Huang
Biodiversity Research Center, Academia Sinica

Chiou-Ju Yao
National Museum of Natural Science

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

Abstract

Evaluation of soundscape variability is essential for acoustic-based biodiversity monitoring. To study biodiversity change, many researchers tried to quantify the complexity of biological sound. However, the analysis of biological sound remains difficult because the soundscape is made up of multiple sound sources. To facilitate the acoustic analysis, we have applied non-negative matrix factorization (NMF) to separate different sound sources in an unsupervised manner. NMF is a self-learning algorithm which factorizes a non-negative matrix as a basis matrix and an encoding matrix. Based on the periodicity information learned from the encoding matrix, biological chorus and the other noise sources can be efficiently separated. Besides, vocalizations of different species can also be separated by using the encoding information learned from multiple layers of NMF and convolutive NMF. In this presentation, we will demonstrate the application of NMF-based blind source separation in the analysis of long-duration field recordings. Our preliminary results suggest that NMF-based blind source separation can effectively recognize biological and non-biological sounds without any learning database. It can also accurately differentiate different vocalizing animals and improve acoustic-based biodiversity monitoring in a noisy environment.

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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.

International Symposium on Grids & Clouds 2017

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

Yu-Huang Wang
Taiwan Biodiversity Information Facility, Biodiversity Research Center, Academia Sinica

Han-Wei Yen
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.

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.

Ecoacoustics 2016

2016/6/5-8 @ University of Michigan

Investigation on the dynamics of soundscape by using unsupervised detection and classification algorithms

Tzu-Hao Lin, Lien-Siang Chou
Institute of Ecology and Evolutionary Biology, National Taiwan University, Repubic of China (Taiwan)

Yu-Huang Wang
Biodiversity Research Center, Academia Sinica, Repubic of China (Taiwan)

Soundscape has been proposed as a potential information source to study the variability of biodiversity. However, analysis of the soundscape is a challenging task when there is no sufficient database to recognize various sounds collected from long duration recordings. Previous researches have measured several acoustic diversity indexes to quantify the variation of biodiversity, but the acoustic diversity indexes are still difficult to interpret without any ground truth. In this study, we propose to analyze the composition of soundscape scenes and visualize the dynamics of soundscape by using unsupervised detection and classification algorithms. Different soundscape scenes were classified according to the tonal sounds, pulsed sounds, and acoustic features obtained from long-term spectrogram. By adjusting the variation explained through classification results, the number of soundscape scenes will be automatically determined. The unsupervised classifier has been employed to analyze the soundscape dynamics in several forests and shallow marine environments in Taiwan. Our results demonstrate that the seasonal and diurnal changing patterns of geophony, biophony, and anthrophony can be effectively investigated. Besides, the spatial change of soundscape can also be discriminated according to the composition of soundscape scenes. After the biophony scenes have been identified, we can apply the same classifier again to measure the complexity of biological sounds and examine the variability of biodiversity. The current approach provides researchers and managers a visualization platform to monitor the dynamics of soundscape and to study the interactions among acoustic environment, biodiversity, and human activities in the future.

2016年臺灣地球科學聯合學術研討會

2016/5/20

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

應用非監督式分類方法調查海洋聲景的時空變化

林子皓
國立台灣大學生態學與演化生物學研究所

海洋聲景由環境音、動物聲音與人為噪音所組成,是由各種水下聲音所構築而成的音響環境。環境音可能來自於風浪、海流、地震等等自然事件,受到海床地形、水文的變化,聲音在各地傳播的路徑有所不同,進而塑造出獨特的音響環境。動物音主要來自於海洋動物發聲,也可能來自動物移動過程或水面活動伴隨發出的聲音。動物音具有高度複雜的變異性,以鯨豚和魚類為例,不同種的聲音特徵有所差別,但同種的聲音卻也有可能受到行為影響而有著不同的結構。海域的人為噪音則以船隻交通、海洋工程的噪音為主,依據接受強度的不同,噪音可能會使動物受到生理傷害、干擾行為、遮蔽溝通,長期暴露下也可能增加免疫壓力。因此,調查海洋聲景不只可以協助我們了解海洋環境特性、海洋動物的種類組成與活動特性,更可以了解人為噪音對海洋生態的影響。近年來隨著水下技術的發展,國際上開始廣泛應用自動錄音機收集長時間水下錄音調查海洋聲景的時空變化。然而目前仍缺乏完整資料庫辨認各種聲音,也難以利用人工分析巨量錄音,因此阻礙了海洋聲景生態學的發展。本研究運用資訊分析技術,解析海洋聲景的事件組成,以進一步了解海洋環境與生態的動態變化。在野外取回水下錄音之後,計算每五分鐘水下錄音的平均功率頻譜,以壓縮大量的錄音資料,並將一系列的平均功率頻譜組合成長期時頻譜圖做為視覺化分析海洋聲景的基礎資料。此外,將每五分鐘的平均功率頻譜作為分析參數,經過多變數分析方法減少特徵向量的維度之後,利用區分資料在多重維度空間內的分佈叢集,作為非監督式分類海洋聲景事件的分析架構。本研究應用自行開發的演算法分析苗栗中港溪口附近海域的水下錄音資料,結果顯示海洋聲景的事件組成在以泥沙底質為主的河口海域以及礁石為主的人工魚礁附近有明顯的結構性差異。海洋聲景在河口海域以較為安靜的環境音、以及夜晚出現的石首魚群鳴唱為主,但在人工魚礁附近則是以吵雜的槍蝦聲音、以及傍晚過後出現的低頻魚群鳴音為主要的事件。透過視覺化分析海洋聲景事件組成的時序變化,將可協助海洋生態研究人員進一步了解各地的海洋動物群聚組成與生態系統的動態變化,並提供海洋生態保育經營的重要基礎資料。

Oceanoise Asia 2016

2016/4/20

Characterization of the marine soundscape at the core habitat of Indo-Pacific humpback dolphins

Tzu-Hao Lin, Lien-Siang Chou
Institute of Ecology and Evolutionary Biology, National Taiwan University

Shane Guan
Office of Protected Resources, National Marine Fisheries Service, Silver Spring, MD, USA

The soundscape in shallow waters displays a high level of spatial variation due to the difference in ocean environments, biological communities, and human activities. Many marine animals rely on sound for orientation; therefore, the soundscape has been hypothesized as one of the environmental indicators for marine animals. The population of Indo-Pacific humpback dolphins in western Taiwan waters is critically endangered. The anthropogenic noise might alter the marine soundscape evidently. However, the importance of soundscape for the habitat selection of cetacean remains unclear until now. In this study, underwater recorders were deployed in inshore waters to compare the difference of soundscape between the core habitat and non-core habitat of humpback dolphins. The result indicates that the composition of soundscape scene is different among our recording stations. At the core habitat, soundscape was characterized by the nighttime chorus of croakers and the quiet ambient sound in the daytime. On the contrary, snapping shrimp sounds represent the most dominated sound at the non-core habitats. The current result indicates that humpback dolphins prefer soundscape dominated by the chorus of their prey resources. The potential impacts of human activities on marine soundscape should be carefully evaluated in the future.