BirdCLEF 2022

Identify bird calls in soundscapes


In this competition, you’ll use your machine learning skills to identify bird species by sound. Specifically, you'll develop a model that can process continuous audio data and then acoustically recognize the species. The best entries will be able to train reliable classifiers with limited training data.

BirdCLEF 2021 - Birdcall IdentificationI

dentify bird calls in soundscape recordings


In this competition, you’ll automate the acoustic identification of birds in soundscape recordings. You'll examine an acoustic dataset to build detectors and classifiers to extract the signals of interest (bird calls). Innovative solutions will be able to do so efficiently and reliably.

Rainforest Connection Species Audio Detection

Automate the detection of bird and frog species in a tropical soundscape

In this competition, you’ll automate the detection of bird and frog species in tropical soundscape recordings. You'll create your models with limited, acoustically complex training data. Rich in more than bird and frog noises, expect to hear an insect or two, which your model will need to filter out.

Cornell Birdcall Identification

Build tools for bird population monitoring


In this competition, you will identify a wide variety of bird vocalizations in soundscape recordings. Due to the complexity of the recordings, they contain weak labels. There might be anthropogenic sounds (e.g., airplane overflights) or other bird and non-bird (e.g., chipmunk) calls in the background, with a particular labeled bird species in the foreground. Bring your new ideas to build effective detectors and classifiers for analyzing complex soundscape recordings!

Freesound Audio Tagging 2019

Automatically recognize sounds and apply tags of varying natures


If successful, your systems could be used for several applications, ranging from automatic labelling of sound collections to the development of systems that automatically tag video content or recognize sound events happening in real time.

The ICML 2013 Bird Challenge

Identify bird species from continuous audio recordings


You are given recordings of 35 species of birds.  The task is to assign a probability that a given species of bird sings at any point in a continuous, 150 second recording.  This is a challenging task because of background noise, variability in the bird sounds, and the fact that the songs overlap.

The Marinexplore and Cornell University Whale Detection Challenge

Create an algorithm to detect North Atlantic right whale calls from audio recordings, prevent collisions with shipping traffic


Marinexplore and Cornell researchers challenge YOU to beat the existing whale detection algorithm identifying the right whale calls. This will advance ship routing decisions in the region.