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.

Advertisements

以國際技術指引探討我離岸風場水下噪音監測評估適用方法

論文發表於2017台灣風能協會會員大會暨學術研討會與科技部成果發表會

以國際技術指引探討我離岸風場水下噪音監測評估適用方法

湛翔智、蕭婷宇、潘柔安
知洋科技有限公司

連永順、王珮蓉、胡芳瑜
工業技術研究院

林子皓
中央研究院

我國政府採「先示範、次潛力、後區塊」三階段離岸風電推動策略,示範獎勵辦法在2012年公告,評選出3案離岸示範風場,接著於2015年公告潛力場址,目前有超過20件開發案提出申請,首先均需通過環評審查。然而各項環評審查項目中,風場開發產生水下噪音對海洋動物影響的問題較為複雜,主要原因是水下噪音監測方法尚無國際標準,困難處是要在海上取得足夠且有效數據,必須有詳實規劃和充分準備,才能利用測量資料來分析水下噪音影響程度。歐洲離岸風電有超過10個國家投入開發,但各國有不同的水下噪音影響評估方式,其中以德國政府提出的技術指引,在監測要求與開發限制上較為嚴格。而歐盟自2008年提出「良好環境狀態(GES)」綱要,提供給歐盟成員國作為管制參考依據,包含離岸風能開發的監測與評估要求。美國投入離岸風能的前期研究多年,直到2016年完成首座風場正式商轉,同年也公告「海洋哺乳類動物聽覺技術指引」,將水下噪音對數種鯨豚的各種影響程度,制訂出水下噪音傷害管制門檻。本研究將探討國際上最新的水下噪音監測與評估技術指引,並依循國內離岸風場的發展情況,提出適用方式的建議供各界參考。

The government devised the offshore wind promotion strategy with 3 phases, which include Demonstration Incentive Program, Zone Application for Planning, and Zonal Development. The demonstration incentive program was announced in 2012, and three projects have been selected accordingly. Then, the Directions of Zone Application for Planning (DZAP) was announced in 2015. More than 20 projects have applied for the DZAP, and they are required to pass environmental impact assessments (EIA) to obtain the first step approval for construction. However, the impact of underwater noise on marine mammal during construction and operation phases are very complex. The key issue is that the underwater noise monitoring method has no international standards and it is difficult to measure sufficient and valid data at sea. Moreover, EIA needs very detailed planning and comprehensive preparation to obtain data and analyze the impact assessment from underwater noise. More than 10 countries in Europe already installed offshore wind farms and employ different assessment methods for underwater noise impact. The requirement of the German government’s technical guidelines for underwater noise monitoring and assessment is most rigid and strict. Since 2008, the EU has proposed the “Good Environmental State (GES)” framework for the member states to achieve requirements for the offshore wind energy development monitoring and evaluation. The United States announced the Marine Mammal Acoustic Technical Guidance in 2016, which provides several acoustic thresholds on different species of marine mammal. This study will discuss the latest international technical guidelines for underwater noise monitoring and assessment. Finally, we carried out the report included the applicable method of underwater noise monitoring and assessment based on the development of offshore wind farms in Taiwan.

論文全文 (pdf)

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.

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

講座地點:清華大學圖書館 1樓清華沙龍(新竹市光復路二段101號)

講座報名:點此報名講座

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!