English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

The work presented in this paper is part of a global framework which longterm goal is to design a wireless sensor network able to support theobservation of a population of endangered birds. We present the first stage forwhich we have conducted a knowledge discovery approach on a sample ofacoustical data. We use MFCC features extracted from bird songs and we exploittwo knowledge discovery techniques. One that relies on clustering-basedapproaches, that highlights the homogeneity in the songs of the species. Theother, based on predictive modeling, that demonstrates the good performances ofvarious machine learning techniques for the identification process. Theknowledge elicited provides promising results to consider a widespread studyand to elicit guidelines for designing a first version of the automaticapproach for data collection based on acoustic sensors.

Song-based Classification techniques for Endangered Bird Conservation