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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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!

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.

Deblending of simultaneous-source seismic data via periodicity-coded nonnegative matrix factorization

Article for IEEE MLSP 2017 DATA CHALLENGE

Deblending of simultaneous-source seismic data via periodicity-coded nonnegative matrix factorization

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

Abstract

To increase the efficiency of seismic acquisition, one needs to break down the simultaneous-source seismic data into single source responses by a procedure called deblending. In this study, we employed the periodicity-coded non-negative matrix factorization (PC-NMF) to separate the primary and the secondary sources recorded in Petroleum Geo-Services dataset. Due to the application of random delay times between consecutive shots, the two sources displayed different patterns among various shots. By combining the response matrices of successive receivers, the PC-NMF can learn the basic components and group the basic components into two clusters according to the periodicity among shot index. Therefore, the deblending of simultaneous-source seismic data can be effectively achieved in an unsupervised manner.

The full paper can be accessed in IEEE DATA PORT.

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: The effects of continuously acoustical stress on cortisol in milkfish

The effects of continuously acoustical stress on cortisol in milkfish (Chanos Chanos)

General and Comparative Endocrinology (2017)
https://doi.org/10.1016/j.ygcen.2017.07.018

Chih An Wei, Yi-Ta Shao*
Institute of Marine Biology, National Taiwan Ocean University

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

Ruo Dong Chen
Institute of Cellular and Organismic Biology, Academia Sinica

Yung-Che Tseng
Marine Research Station, Institute of Cellular and Organismic Biology, Academia Sinica

Strong underwater acoustic noise has been known that may cause hearing loss and actual stress in teleost. However, the long-term physiological effects of relatively quiet but continuously noise on fish were less understood. In present study, milkfish, Chanos chanos, were exposed to the simulated-wind farm noise either quiet (109 dB re 1 μPa / 125.4 Hz; approx. 10-100m distant from the wind farm) or noisy (138 dB re 1 μPa / 125.4 Hz; nearby the wind farm) conditions for 24 hr, 3 days and 1 week. Comparing to the control group (80 dB re 1 μPa / 125.4 Hz), the fish exposed to noisy conditions had higher plasma cortisol levels in the first 24 hr. However, the cortisol levels of 24 hr spot returned to the resting levels quickly. The fish exposed under noisy condition had significantly higher head kidney star (steroidogenic acute regulatory) and hsd11b2 (11-β-hydroxysteroid dehydrogenase 2) mRNA levels at the following treatment time points. In addition, noise exposure did not change hypothalamus crh (Corticotropin-releasing hormone) mRNA levels in this experiment. The results implied that the weak but continuously noise was a potential stressor to fish, but the impacts may be various depending on the sound levels and exposure time. Furthermore, this study showed that the continuous noise may up-regulate the genes that are related to cortisol synthesis and possibly make the fish more sensitive to ambient stressors, which may influence the energy allocation appearance in long-term exposures.