Source separation in ecoacoustics: a roadmap towards versatile soundscape information retrieval

• Published in: Remote sensing in ecology and conservation 2020-09, Vol.6 (3), p.236-247
• Main Authors: Lin, Tzu‐Hao, Tsao, Yu, Pettorelli, Nathalie
• Format: Article
• Language: English
• Subjects: acoustic habitat; Acoustics; Anthropogenic factors; Audio data; Biodiversity; Biomonitoring; Ecological effects; Ecological monitoring; Ecosystem assessment; Ecosystem dynamics; Environmental changes; Human influences; Information processing; Information retrieval; Machine learning; Music; Noise; Nonlinear filters; passive acoustics; Principal components analysis; Separation; Separation techniques; Signal processing; Sound; Sound filters; Sound sources; Sparsity
• ISSN: 2056-3485; 2056-3485
• DOI: 10.1002/rse2.141

A comprehensive assessment of ecosystem dynamics requires the monitoring of biological, physical and social changes. Changes that cannot be observed visually may be trackable acoustically through soundscape analysis. Soundscapes vary greatly depending on geophysical events, biodiversity and human activities. However, retrieving source‐specific information from geophony, biophony and anthropophony remains a challenging task, due to interference by simultaneous sound sources. Audio source separation is a technique that aims to recover individual sound sources when only mixtures are accessible. Here, we review techniques of monoaural audio source separation with the fundamental theories and assumptions behind them. Depending on the availability of prior information about the source signals, the task can be approached as a blind source separation or a model‐based source separation. Most blind source separation techniques depend on assumptions about the behaviour of the source signals, and their performance may deteriorate when the assumptions fail. Model‐based techniques generally do not require specific assumptions, and the models are directly learned from labelled data. With the recent advances of deep learning, the model‐based techniques can yield state‐of‐the‐art separation performance, accordingly facilitate content‐based audio information retrieval. Source separation techniques have been adopted in several ecoacoustic applications to evaluate the contributions from biodiversity and anthropogenic disturbance to soundscape dynamics. They can also be employed as nonlinear filters to improve the recognition of bioacoustic signals. To effectively retrieve ecological information from soundscapes, source separation is a crucial tool. We believe that the future integrations of ecological hypotheses and deep learning can realize a high‐performance source separation for ecoacoustics, and accordingly improve soundscape‐based ecosystem monitoring. Therefore, we outline a roadmap for applying source separation to assist in soundscape information retrieval and hope to promote cross‐disciplinary collaboration. Retrieving source‐specific information from geophony, biophony and anthrophony from soundscapes remains a challenging task. This study reviews modern techniques of monoaural audio source separation with their assumptions and limitations. The review shows that source separation techniques are crucial for assisting the evaluation of soundscape dynamics, and the aut