A machine learning pipeline for classification of cetacean echolocation clicks in large underwater acoustic datasets
Machine learning algorithms, including recent advances in deep learning, are promising for tools for detection and classification of broadband high frequency signals in passive acoustic recordings. However, these methods are generally data-hungry and progress has been limited by challenges related t...
Main Author: | Frasier, K.E (Author) |
---|---|
Format: | Article |
Language: | English |
Published: |
Public Library of Science
2021
|
Subjects: | |
Online Access: | View Fulltext in Publisher |
Similar Items
-
Clustering a database of optically absorbing organic molecules via a hierarchical fingerprint scheme that categorizes similar functional molecular fragments
by: Cole, J.M, et al.
Published: (2022) -
Acoustic characteristics of northern and southern resident killer whale echolocation clicks
by: Lawson, Jack
Published: (2021) -
OutlierNets: Highly Compact Deep Autoencoder Network Architectures for On-Device Acoustic Anomaly Detection
by: Saad Abbasi, et al.
Published: (2021-07-01) -
Robust clustering algorithms
by: Gupta, Pramod
Published: (2011) -
An application of machine learning with feature selection to improve diagnosis and classification of neurodegenerative disorders
by: Josefa Díaz Álvarez, et al.
Published: (2019-10-01)