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.

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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.

Improving biodiversity monitoring through soundscape information retrieval

Presented in International Symposium on Grids & Clouds 2018

Improving biodiversity monitoring through soundscape information retrieval

 Yu Tsao1, Tzu-Hao Lin1, Mao-Ning Tuanmu2, Joe Chun-Chia Huang2, Chia-Yun Lee2, Chiou-Ju Yao3

1Research Center for Information Technology Innovation, Academia Sinica
2Biodiversity Research Center, Academia Sinica
3National Museum of Natural Science

Passive acoustic monitoring has been suggested as an effective tool for investigating the dynamics of biodiversity. For instance, automatic detection and classification of sounds can acquire information of species occurrences and behavioral activities of vocalizing animals. However, current methods of automatic acoustic identification of species remain uncertain for most taxa, which constrains the application of remote acoustic sensing in biodiversity monitoring. One challenge is that most of the training samples more-or-less contains undesired sound signals from non-target sources. To overcome this issue, we developed a source separation algorithm based on a deep version of non-negative matrix factorization (NMF). Using multiple layers of convolutive NMF to learn spectral features and temporal modulation of sound signals from a spectrogram, vocalizations of different species can be effectively separated in an unsupervised manner. Based on the pre-trained features, acoustic activities of target species can be efficiently separated from long-duration field recordings. Besides, spectral features of each vocalizing species can also be archived for further utilizations. In this presentation, we will demonstrate the application of deep NMF on separating sounds from different species for both birds and bats. Our results show that the proposed deep NMF approach can be used to establish recognition database of vocalizing animals for soundscape-based biodiversity monitoring, confirming its promising applicability for the field of soundscape information retrieval.

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.

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.

New article online: Improving biodiversity assessment via unsupervised separation of biological sounds from long-duration recordings

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.)

Shih-Hua Fang
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.

 

聆聽大自然四季之音

「聆聽,森林的生命與故事」—系列講座

透過自動錄音機一年365天,從早到晚,每30分鐘錄下5分鐘的檔案,讓我們有機會透過聲音更認識自然谷的風吹草動、燕語鶯啼、龍吟虎嘯。藉著錄音機,錄下土地四季的聲音波動,更進一步運用聲波圖看到四季天氣變化。

講座時間:6月22日(四)晚上0700-0930

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對此講座有興趣的朋友,也歡迎閱讀 用「聲物」識生物 以自動錄音聆聽自然谷之聲 這篇文章,在文章裡面,我們簡單的分享了如何應用自動錄音機探索自然谷的豐富生物多樣性,也可以到我們所製作的互動式網頁中,一探自然谷的各種聲物!