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.

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Studying wildlife activities by using soundscape information

Oral presentation in 2018 ICEO & SI Conference

Studying wildlife activities by using soundscape information

Tzu-Hao Lin1, Yu Tsao2, Chun-Chia Huang3, and Mao-Ning Tuanmu3

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

Information regarding biodiversity change is essential for the decision making of resource exploitation and conservation management. Studies on biodiversity are labor-intensive and time-consuming. Therefore, the development of a remote observation platform of wildlife is essential. In recent decades, passive acoustic monitoring has been widely employed to detect vocalizing animals. Besides, various environmental sounds and anthropogenic noises can also be recorded in a soundscape. Thus, a soundscape monitoring network has been considered as an acoustic sensing platform of the ecosystem. Although a significant amount of acoustic data can be collected, the acoustic data analysis remains a challenge for ecologists. In this study, we employed multiple layers of non-negative matrix factorization (NMF) to decompose a spectrogram into individual sound sources. Our results showed that echolocation calls produced from three different bat species can be effectively separated in an unsupervised manner. Even for overlapping signals, the deep NMF can still produce a reliable separation result. Therefore, the integration of NMF-based blind source separation and a soundscape monitoring network can reduce the difficulty of acoustic-based wildlife monitoring in the future.

The full text is available: https://drive.google.com/file/d/1Llf9RuyeR4a7k36p_MMj9OZ2apaeZ_iz/view

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.

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.

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!