scholarly journals Seismic savanna: machine learning for classifying wildlife and behaviours using ground‐based vibration field recordings

2021 ◽  
Author(s):  
Alexandre Szenicer ◽  
Michael Reinwald ◽  
Ben Moseley ◽  
Tarje Nissen‐Meyer ◽  
Zachary Mutinda Muteti ◽  
...  
Keyword(s):  
Machine Learning ◽  
Field Recordings ◽  
Vibration Field

Using a Sonic Time-Lapse Method as a Compositional Tool

2020 ◽  
Vol 25 (2) ◽  
pp. 198-204
Author(s):  
Felipe Otondo ◽  
Victor Poblete
Keyword(s):  
Machine Learning ◽  
Time Lapse ◽  
Machine Learning Techniques ◽  
Field Recordings ◽  
Future Developments ◽  
Learning Techniques ◽  
Natural Sense ◽  
Continuous Field ◽  
Compositional Method ◽  
Time Future

This article describes an innovative compositional method based on the use of a sonic time-lapse algorithm to create soundscape audio montages. The method is based on the superposition of short audio samples of 24-hour continuous field recordings carried out in various kinds of wildlife sonic environments. The optimisation of the algorithm focused on the enhancement of gradual crossfade transitions between recorded samples and the use of variable sample durations as a way of recreating a natural sense of evolution of sonic events in time. Future developments of the project will integrate virtual reality interfaces and environmental education projects as part of the time-lapse algorithm machine-learning techniques and also creative tools suitable for multimedia installations.

Mind wandering as data augmentation: How mental travel supports abstraction

2020 ◽  
Vol 43 ◽  
Author(s):  
Myrthe Faber
Keyword(s):  
Machine Learning ◽  
Data Augmentation ◽  
Mental Content ◽  
Mind Wandering ◽  
Theoretical Framework ◽  
Important Addition

Abstract Gilead et al. state that abstraction supports mental travel, and that mental travel critically relies on abstraction. I propose an important addition to this theoretical framework, namely that mental travel might also support abstraction. Specifically, I argue that spontaneous mental travel (mind wandering), much like data augmentation in machine learning, provides variability in mental content and context necessary for abstraction.

Data Mining and Machine Learning

2020 ◽  
Author(s):  
Mohammed J. Zaki ◽  
Wagner Meira, Jr
Keyword(s):  
Machine Learning ◽  
Data Mining

Mathematics for Machine Learning

2020 ◽  
Author(s):  
Marc Peter Deisenroth ◽  
A. Aldo Faisal ◽  
Cheng Soon Ong
Keyword(s):  
Machine Learning

Phase Transitions in Machine Learning

2009 ◽  
Author(s):  
Lorenza Saitta ◽  
Attilio Giordana ◽  
Antoine Cornuejols
Keyword(s):  
Machine Learning ◽  
Phase Transitions

Understanding Machine Learning

2009 ◽  
Author(s):  
Shai Shalev-Shwartz ◽  
Shai Ben-David
Keyword(s):  
Machine Learning

Using machine learning techniques to reduce data annotation time

2006 ◽  
Author(s):  
Christopher Schreiner ◽  
Kari Torkkola ◽  
Mike Gardner ◽  
Keshu Zhang
Keyword(s):  
Machine Learning ◽  
Machine Learning Techniques ◽  
Data Annotation ◽  
Learning Techniques
Sign in / Sign up

Export Citation Format

Share Document