Training workshop on the acoustical analysis of animal vocalizations

Time: 2018/07/04 (Wed)
Location: Biodiversity Research Center, Academia Sinica
Speaker: Dr. Tzu-Hao Lin (Department of Marine Biodiversity Research, JAMSTEC)

Morning Session: Passive acoustic monitoring
09:00-09:30: Registration
09:30-10:00: Passive acoustic monitoring of wildlife (Lecture)
10:00-10:30: Labeling of biosonar signal (Practice)
10:30-11:30: Automatic detection of biosonar activity (Practice)
11:30-12:00: Discussion
12:00-13:30: Lunch (自理)

Afternoon Session: Application of PAM in an offshore wind farm
13:30-14:00: Passive acoustic monitoring of soniferous marine animals in an offshore wind farm (Lecture)
14:00-15:00: Searching dolphin biosonars and fish sounds from long-duration recordings (Practice)
15:00-15:30: Break
15:30-16:00: Temporal analysis of acoustic detection results (Practice)
16:00-16:30: Discussion

Please go to this link for registering this training workshop. Due to the limited space, only 25 seats are available. The final attendant list will be determined by the organizer and only those successful registers will be notified by email.

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Information retrieval of marine soundscape via unsupervised source separation

Oral presentation in 2nd Oceanoise Asia at Hakodate, Japan

Information retrieval of marine soundscape via unsupervised source separation

Tzu-Hao Lin1, Tomonari Akamatsu2, Yu Tsao1

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

Soundscape, which is a collection of biophony, geophony, and anthrophony, has been considered as a passive acoustic sensing platform of our marine ecosystem. In recent years, different soundscape indices have been developed to describe the variability of a soundscape. However, the analysis of soundscape remains a challenge because of the influence of simultaneous sound sources. Simultaneous sound sources can be separated by using supervised and unsupervised machine learning approaches. Supervised separation requires reference recordings with good signal-to-noise ratio, which is difficult to collect in marine environments. On the other hand, unsupervised separation may be a feasible solution for the analysis of marine soundscape because no prior information is needed. In this presentation, we will demonstrate the application of nonnegative matrix factorization (NMF), a machine learning algorithm, models spectral features and time encoding information from a spectrogram, for the unsupervised separation of marine soundscape. Periodicity-coded NMF (PC-NMF) is a two-stage NMF that aims to separate biological choruses from other noise sources according to the different periodicity properties of diurnal cycles. By using the PC-NMF, croaker choruses can be isolated from a long-term spectrogram interfered with strong mooring noises. We compared the analysis result of PC-NMF with that of a rule-based fish sound detector. The comparison shows that the PC-NMF and the rule-based detector performed consistently in quiet recording conditions. In noisy situations, the PC-NMF and the rule-based detector tend to report false negatives and false positives, respectively. In addition to biological choruses, other noise sources separated by the PC-NMF also give us insights regarding the variability of environmental and anthropogenic sounds. The results suggest that information retrieval from marine soundscape will be possible by using the NMF-based separation algorithm.

Listening to the deep: Exploring marine soundscape variability by information retrieval techniques

Presentation in the session of Lidar and Passive observing sensors, Oceans’18 Kobe

Listening to the deep: Exploring marine soundscape variability by information retrieval techniques

Tzu-Hao Lin1, Yu Tsao2

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

Information on the dynamics of the deep-sea ecosystem is essential for conservation management. The marine soundscape has been considered as an acoustical sensing
platform to investigate geophysical events, marine biodiversity, and human activities. However, analysis of the marine soundscape remains difficult because of the influence of simultaneous sound sources. In this study, we integrated machine learning-based information retrieval techniques to analyze the variability of the marine soundscape off northeastern Taiwan. A long-term spectral average was employed to visualize the longduration recordings of the Marine Cable Hosted Observatory (MACHO). Biotic and abiotic soundscape components were separated by applying periodicity-coded nonnegative matrix factorization. Finally, various acoustic events were identified
using k-means clustering. Our results show that the MACHO recordings of June 2012 contain multiple sound sources. Cetacean vocalizations, an unidentified biological chorus, environmental noise, and system noise can be accurately separated without an audio recognition database. Cetacean vocalizations were primarily detected at night, which is consistent with the detection results of two rule-based detectors. The unidentified biological chorus, ranging between 2 and 3 kHz, was primarily recorded between 7 p.m. and midnight during the studied period. On the basis of source separation, more acoustic events can be identified in the clustering result. The proposed
information retrieval techniques effectively reduce the difficulty in the analysis of marine soundscape. The unsupervised approach of source separation and clustering can improve the investigation regarding the temporal behavior and spectral characteristics of different sound sources. Based on the findings in the present study, we believe that variability of the deep-sea ecosystem can be efficiently investigated by combining the
soundscape information retrieval techniques and cabled hydrophone networks in the future.

被動式水下聲學監測離岸風場海洋動物生態活動

口頭發表於第20屆水下技術研討會

被動式水下聲學監測離岸風場海洋動物生態活動

林子皓[1] 楊信得[2] 黃鈞漢[3] 姚秋如[4]

[1] 日本國立研究開發法人海洋研究開發機構博士後研究員
[2] 觀察家生態顧問有限公司水域部專員
[3] 觀察家生態顧問有限公司水域部經理
[4] 國立自然科學博物館助理研究員

離岸風能開發已成為台灣的重要再生能源政策之一,但如何在公開透明的前提下探討離岸風場海洋開發與海洋生態的交互作用仍是一大難題。被動式水下聲學監測已經被廣泛的應用於海洋動物生態研究,如:鯨豚、發聲魚類、槍蝦等動物,透過系統性、標準化方法所取得的水下錄音,將可以做為長期生態監測的數位化公開資料,並可供所有權益關係人重複檢驗。本研究應用基於預定義規則的能量偵測器與基於長期時頻譜資料的週期性編碼非負矩陣分解法,比較兩種演算法在偵測鯨豚與石首魚聲音的效能差異。結果顯示,兩種方法在高訊噪比的情況下皆能有效偵測,但在低訊噪比的情況下則有不同程度的偏差。因此,建議未來在進行離岸風場的水下聲學監測時,除了可使用多種偵測工具評估分析結果之外,也必須公開部分的水下錄音與動物聲音標籤資料,以協助權益關係人重複檢驗動物聲音偵測結果的可信度。未來,透過標準化的生態指標分析將能夠提高離岸風場生態環境監測資料的價值,做為後續進行生態檢核與保育經營管理的重要依據。

New article online: Comparison of passive acoustic soniferous fish monitoring with supervised and unsupervised approaches

Comparison of passive acoustic soniferous fish monitoring with supervised and unsupervised approaches

The Journal of the Acoustical Society of America 143, EL278 (2018)
https://doi.org/10.1121/1.5034169

Tzu-Hao Lin
Department of Marine Biodiversity Research, Japan Agency for Marine-Earth Science and Technology, 2-15, Natsushima, Yokosuka City, Kanagawa, 237-0061, Japan

Yu Tsao
Research Center for Information Technology Innovation, Academia Sinica, Number 128, Section 2, Academia Road, Taipei 115, Taiwan, Republic of China

Tomonari Akamatsu
National Research Institute of Fisheries Science, Japan Fisheries Research and Education Agency, 2-12-4 Fukuura, Yokohama, Kanagawa 236-8648, Japan

Passive acoustics has been used to investigate behavior and relative abundances of soniferous fish. However, because of noise interferences, it remains challenging to accurately analyze acoustic activities of soniferous fish. This study proposes a multi-method approach, which combines rule-based detector, periodicity-coded non-negative matrix factorization, and Gaussian mixture models. Although the three methods performed well when used to detect croaker choruses in quiet conditions, inconsistent results are observed in noisy conditions. A consistency matrix can provide insights regarding the bias of acoustic monitoring results. The results suggest that the proposed approach can reasonably improve passive acoustic monitoring of soniferous fish.

A new dive @ JAMSTEC !

From this month, I will start my new position at JAMSTEC as an international postdoctoral research fellow. At JAMSTEC, I will keep working on the soundscape-based biodiversity monitoring. Of course, most of the efforts will focus on marine ecosystem.

However, I do believe that the tools we developed should be able to facilitate the ecoacoustics researches in terrestrial ecosystems. So please contact me if you are interested in ecoacoustics or ecological informatics! I am very happy to collaborate with researchers from different disciplines!

從這個月開始,我將會從中研院資創中心轉換工作到日本國立研究法人海洋研究開發機構(JAMSTEC),擔任國際博士後研究員。在JAMSTEC我還是會持續以聲景為基礎的生物多樣性監測,只是大部分的時間都會主要專注於海洋生態系上(才不會不務正業…XD)。

不過,就如同我過去在資創中心進行的工作,我們未來開發的分析工具都還是會希望可以應用於各種不同環境,不管是海域或是陸域。如果是對於生態聲學、生態資訊學有興趣的朋友,都歡迎與我聯繫,我非常希望能夠和各種不同領域的研究人員一起合作、學習!

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.