Imperial College London

Dr Sarab Sethi

Faculty of Natural SciencesDepartment of Mathematics

Academic Visitor



s.sethi16 CV




Electrical EngineeringSouth Kensington Campus





I work on real-time continuous bioacoustic monitoring of ecosystems. This has included developing and deploying robust acoustic monitoring units to the field, and interpreting the audio data with machine learning approaches to quantify the biodiversity of a region.

A network of these sensors is deployed in the Stability of Altered Forest Ecosystems (SAFE) Project, located in the tropical rainforests of Borneo, to help understand the effect of changing land use upon local wildlife. You can listen to audio from our acoustic monitoring network at the SAFE Acoustics website.

I am leading a project, Bugg, exploring commercialisation of the eco-acoustic monitoring technology developed as part of my research.

I am also involved with work developing tools and approaches for cross-disciplinary highly comparative time-series analysis (hctsa), including the analyses behind the CompEngine website.

Visiting lecturer for Innovation Design Engineering (IDE) and Global Innovation Design (GID) M.A. / M.Sc. joint courses at Imperial College London and the Royal College of Arts.



Heath BE, Orme DS, Sethi CSL, et al., 2021, How index selection, compression, and recording schedule impact the description of ecological soundscapes, Evolutionary Ecology, Vol:11, ISSN:0269-7653, Pages:13206-13217

Sethi S, Ewers R, Jones N, et al., 2020, SAFE Acoustics: an open-source, real-time eco-acoustic monitoring network in the tropical rainforests of Borneo, Methods in Ecology and Evolution, Vol:11, ISSN:2041-210X, Pages:1182-1185

Sethi S, Jones NS, Fulcher B, et al., 2020, Characterising soundscapes across diverse ecosystems using a universal acoustic feature set, Proceedings of the National Academy of Sciences of Usa, Vol:117, ISSN:0027-8424, Pages:17049-17055

Fulcher B, Lubba C, Sethi S, et al., 2020, A self-organizing, living library of time-series data, Scientific Data, Vol:7, ISSN:2052-4463

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