2024
DOI: 10.1101/2024.11.07.622465
|View full text |Cite
Preprint
|
Sign up to set email alerts
|

Interpretable and Robust Machine Learning for Exploring and Classifying Soundscape Data

Arpit Omprakash,
Rohini Balakrishnan,
Robert Ewers
et al.

Abstract: The adoption of machine learning in Passive Acoustic Monitoring (PAM) has improved prediction accuracy for tasks like species-specific call detection and habitat quality estimation. However, these models often lack interpretability, and PAM generates vast amounts of non-informative data, as soundscapes are typically information sparse. Here, we developed ecologically interpretable methods that accurately predict land use from audio while filtering unwanted data. Audio from habitats in Southern India (evergreen… Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...

Citation Types

0
0
0

Publication Types

Select...

Relationship

0
0

Authors

Journals

citations
Cited by 0 publications
references
References 38 publications
0
0
0
Order By: Relevance

No citations

Set email alert for when this publication receives citations?