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.

 

2017年海洋科學年會

2017/5/4-5 @ 國立中山大學

聆聽海洋的訊息:應用深度學習分析海洋聲景之變動

林子皓、曹昱
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。

5th Joint Meeting of the Acoustical Society of America and Acoustical Society of Japan

2016/11/28-12/2 @ Honolulu, USA

Acoustic response of Indo-Pacific humpback dolphins to the variability of marine soundscape

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

Shih-Hau Fang
Department of Electrical Engineering, Yuan Ze University

Chih-Kai Yang, Lien-Siang Chou
Institute of Ecology and Evolutionary Biology, National Taiwan University

Marine mammals can adjust their vocal behaviors when they encounter anthropogenic noise. The acoustic divergence among different populations has also been considered as the effect of ambient noise. The recent studies discover that the marine soundscape is highly dynamic; however, it remains unclear how marine mammals alter their vocal behaviors under various acoustic environments. In this study, autonomous sound recorders were deployed in western Taiwan waters between 2012 and 2015. Soundscape scenes were unsupervised classified according to acoustic features measured in each 5 min interval. Non-negative matrix factorization was used to separate different scenes and to inverse the temporal occurrence of each soundscape scene. Echolocation clicks and whistles of Indo-Pacific humpback dolphins, which represent the only marine mammal species occurred in the study area, were automatically detected and analyzed. The preliminary result indicates the soundscape scenes dominated by biological sounds are correlated with the acoustic detection rate of humpback dolphins. Besides, the dolphin whistles are much complex when the prey associated scene is prominent in the local soundscape. In the future, the soundscape information may be used to predict the occurrence and habitat use of marine mammals.

2017年動物行為生態研討會

2017/1/23-24 @ 高雄中山大學

應用機器學習探討海洋聲景變動與中華白海豚發聲活動之關聯

林子皓、曹昱
中央研究院資訊科技創新研究中心

方士豪
元智大學電機工程學系

鯨豚的發聲行為相當多變,不同族群可能會在各種環境音改變哨聲特徵﹐也會在遭遇人為噪音時改變聲音結構。海洋聲景是由環境音、動物音與人為噪音組成,具有高度變異的特性。儘管過去已有不少針對鯨豚發聲與單一音源的研究,但是對鯨豚如何在多變的海洋聲景且多重聲源相互重疊的狀況下改變行為仍不清楚。本研究透過水下錄音機,長期收錄2014年苗栗海域的海洋錄音。首先應用自動偵測器尋找中華白海豚水下聲音,再應用非負矩陣分解法學習海洋聲景中的主要聲源特徵。透過非監督式學習器,可以有效拆解長期時頻譜圖,視覺化呈現石首魚鳴唱、槍蝦聲音、環境與人為噪音等主要聲源的相對變化。利用廣義疊加模型分析聲景與白海豚聲音後,我們發現白海豚的聲音偵測率與複雜度和各種聲源皆有不同的相關性。此結果顯示應用機器學習分離聲景中的各種聲源之後,將能夠有效瞭解動物和各種聲源的交互作用。未來,聲景中的各種訊息也可以作為預測動物活動的生態遙測資料。

Ecoacoustics 2016

2016/6/5-8 @ University of Michigan

Investigation on the dynamics of soundscape by using unsupervised detection and classification algorithms

Tzu-Hao Lin, Lien-Siang Chou
Institute of Ecology and Evolutionary Biology, National Taiwan University, Repubic of China (Taiwan)

Yu-Huang Wang
Biodiversity Research Center, Academia Sinica, Repubic of China (Taiwan)

Soundscape has been proposed as a potential information source to study the variability of biodiversity. However, analysis of the soundscape is a challenging task when there is no sufficient database to recognize various sounds collected from long duration recordings. Previous researches have measured several acoustic diversity indexes to quantify the variation of biodiversity, but the acoustic diversity indexes are still difficult to interpret without any ground truth. In this study, we propose to analyze the composition of soundscape scenes and visualize the dynamics of soundscape by using unsupervised detection and classification algorithms. Different soundscape scenes were classified according to the tonal sounds, pulsed sounds, and acoustic features obtained from long-term spectrogram. By adjusting the variation explained through classification results, the number of soundscape scenes will be automatically determined. The unsupervised classifier has been employed to analyze the soundscape dynamics in several forests and shallow marine environments in Taiwan. Our results demonstrate that the seasonal and diurnal changing patterns of geophony, biophony, and anthrophony can be effectively investigated. Besides, the spatial change of soundscape can also be discriminated according to the composition of soundscape scenes. After the biophony scenes have been identified, we can apply the same classifier again to measure the complexity of biological sounds and examine the variability of biodiversity. The current approach provides researchers and managers a visualization platform to monitor the dynamics of soundscape and to study the interactions among acoustic environment, biodiversity, and human activities in the future.

2016 動物行為暨生態研討會

2016/1/25-26

中華白海豚核心棲地的海洋聲景特徵以及保育經營的應用

林子皓、周蓮香

國立台灣大學生態學與演化生物學研究所

Shane Guan

美國國家海洋漁業局保護資源辦公室

海洋聲景由環境音、動物音以及人為噪音所組成,物理環境影響了聲音傳播,各地不同的動物群聚與人類活動也塑造出各種獨特的聲景。聲景中的訊息可讓海洋動物尋找適合的棲地,察知其他個體的活動位置,甚至探測獵物位置,因此可說是海洋動物生存的重要資源之一。台灣西部淺海的中華白海豚族群面臨許多威脅,水下噪音除了可能造成聽力損傷、行為干擾之外,也會改變當地的海洋聲景。但目前仍缺乏對白海豚棲地的聲景研究,也不清楚聲景的變化是否影響白海豚的棲地選擇。本研究於苗栗縣海域收集長時間水下聲音,分析時頻譜圖的時空變化,發現中港溪口的聲景與其他地區之間有明顯差異。中港溪口是中華白海豚的核心棲地,聲景以白天安靜的環境音和夜間吵雜的石首魚鳴唱為主。白海豚鮮少活動在龍鳳漁港外海的定置網區,以及外埔漁港外海的魚礁區,當地聲景則以槍蝦的寬頻脈衝聲與船隻噪音為主,石首魚的夜間鳴唱也較為低頻且不明顯。本研究結果顯示海洋聲景確實在白海豚核心與非核心棲地之間存在差異,高強度的石首魚鳴唱可能代表充足的食餌資源。未來除了可透過海洋聲景了解白海豚的潛在棲地,也可利用水下監聽站自動監測白海豚、石首魚與人為活動在各地海域的動態變化,以協助中華白海豚重要棲息環境的保育經營。

Conference presentation: 21st Biennial Conference on the Biology of Marine Mammals

13-18 Dec 2015

A noisy dinner? Passive acoustic monitoring on the predator-prey interactions between Indo-Pacific humpback dolphins and croakers

Tzu-Hao Lin, Wen-Ching Lien, Chih-Kai Yang, and Lien-Siang Chou
Institute of Ecology and Evolutionary Biology, National Taiwan University

Shane Guan
Office of Protected Resources, National Marine Fisheries Service, Silver Spring, MD, USA

The spatio-temporal dynamics of prey resources have been considered as important factors for shaping the distribution and behavior of odontocetes. Indo-Pacific humpback dolphin (Sousa chinensis) is a costal species, which primary feeds on benthic croakers. It has been hypothesized that the distribution pattern and periodic occurrence of humpback dolphins are results of their prey movement. However, the interactions between humpback dolphins and croakers remain unclear. During May 2013 and November 2014, underwater sound recordings were collected in western Taiwan waters. Croaker choruses and humpback dolphin echolocation clicks were automatically detected using custom developed algorithms. Both croaker choruses and dolphin clicks were frequently detected in shallow estuarine waters during spring and summer. In addition, shorter inter-click intervals were detected with higher frequencies in these areas, indicating more likely foraging behavior. Current results suggest that the core habitats of humpback dolphins show an agreement with the areas of prominent croaker chorus. Diurnal cycle analysis showed that croaker choruses were most evident after sunset to until approximately 4 A.M. In estuarine waters, humpback dolphin clicks were most frequently detected during the nighttime, with reduced detection rates after 8 A.M. This suggests that the diurnal behavior of humpback dolphins could be associated with the calling behavior of croakers. Although whether the position of a calling croaker could be passively localized by a dolphin remains unknown, our results indicate that the foraging probability of humpback dolphins may be elevated during the nighttime chorus of croakers. Information regarding the spatio-temporal dynamics of croaker chorus can be important for the conservation management of humpback dolphins. Further details on the predator-prey interactions between humpback dolphins and croakers can be investigated by using hydrophone arrays.

Poster (pdf)