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.


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.



2017/5/4-5 @ 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。

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.


2017/1/23-24 @ 高雄中山大學





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





Shane Guan