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


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.






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)


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!


2017/5/4-5 @ 國立中山大學


中央研究院 資訊科技創新研究中心

被動式聲學監測已被廣泛應用在海洋環境與生態研究中,長期錄音中的各種環境音與動物音增加了我們對海洋生態環境的了解,許多研究也深入探討人為噪音對海洋生態的影響。然而,過去針對海洋聲景的分析大多著重噪音的時頻譜特性,並透過設定規則的偵測器尋找海洋動物的聲音。但海洋聲景受到地形、氣候、生物群聚與人為活動的高度影響,時頻譜分析可能無法有效描述同時出現的多種聲源,偵測器效能也隨著噪音變動而改變。為了有效分離海洋聲景中的各種聲源,本研究應用非負矩陣分解法 (non-negative matrix factorization) 及其變形方法分析長期時頻譜圖,將輸入資料拆解為特徵矩陣與編碼矩陣。雖然單層的非負矩陣分解法在多次疊代後,能夠在特徵矩陣與編碼矩陣約略學習到各種聲源的頻譜特徵與時域上的強度,但仍難以分離重疊的多種聲源。本研究將多層學習器分別預訓練後堆疊成深度學習架構,並在各層之間逐漸減少特徵矩陣之基底數量,藉由最末層回傳後之重建資料和輸入資料的誤差,在多次疊代中自行修正各層模型參數以達到最佳的聲源分離成果。本研究針對各地具有不同環境噪音特性的海洋聲景進行分析,結果顯示在不需要辨識樣本與資料標籤的情況下,深度學習能夠有效分離海洋中的各種主要聲源:魚群鳴唱、槍蝦脈衝聲、船隻噪音與環境音。學習到的特徵矩陣也能夠作為辨識樣本,透過半監督式學習分析大量的線上資料。透過深度學習分離聲源,未來將能夠更有效評估海洋聲景的複雜結構,並藉此探討海洋環境與生態的變動,以及人為開發的影響。

International Symposium on Grids & Clouds 2017

2017/3/5-10 @ Academia Sinica, Taipei, Taiwan

Listening to the ecosystem: the integration of machine learning and a long-term soundscape monitoring network

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

Yu-Huang Wang
Taiwan Biodiversity Information Facility, Biodiversity Research Center, Academia Sinica

Han-Wei Yen
Academia Sinica Grid Computing

Information on the variability of environment and biodiversity is essential for conservation management. In recent years, soundscape monitoring has been proposed as a new approach to assess the dynamics of biodiversity. Soundscape is the collection of biological sound, environmental sound, and anthropogenic noise, which provide us the essential information regarding the nature environment, behavior of calling animals, and human activities. The recent developments of recording networks facilitate the field surveys in remote forests and deep marine environments. However, analysis of big acoustic data is still a challenging task due to the lack of sufficient database to recognize various animal vocalizations. Therefore, we have developed three tools for analyzing and visualizing soundscape data: (1) long-term spectrogram viewer, (2) biological chorus detector, (3) soundscape event classifier. The long-term spectrogram viewer helps users to visualize weeks or months of recordings and evaluate the dynamics of soundscape. The biological chorus detector can automatically recognize the biological chorus without any sound template. We can separate the biological chorus and non-biological noise from a long-term spectrogram and unsupervised identify various biological events by using the soundscape event classifier. We have applied these tools on terrestrial and marine recordings collected in Taiwan to investigate the variability of environment and biodiversity. In the future, we will integrate these tools with the Asian Soundscape monitoring network. Through the open data of soundscape, we hope to provide ecological researcher and citizens an interactive platform to study the dynamics of ecosystem and the interactions among acoustic environment, biodiversity, and human activities.