Using Machine Learning to identify bird calls in audio recordings
Welcome to Chirpity, your ultimate companion for identifying bird vocalisations. Designed with Nocmig enthusiasts and bioacoustic researchers in mind, Chirpity revolutionises the process of reviewing extensive audio files for the presence of avian sounds. Powered by cutting-edge Machine Learning, Chirpity offers the choice between the well-known BirdNET and a bespoke Nocmig model - finely tuned for Nocturnal Flight Calls - ensuring identification tailored to your specific needs. With intuitive features for reviewing, tagging, and organizing detected calls, Chirpity saves valuable time and effort. Available for Windows, Mac and Linux platforms, download Chirpity today!
Many people familiar with BirdNET have used the "BirdNET Analyzer GUI" for automated bird call identification. Due to many optimisations made in the application, Chirpity is able to perform the analysis significantly faster. The following benchmark data show the time taken to analyse a file just over 3 hours long. Firstly using the native Chirpity detection model, then BirdNET within Chirpity, and finally BirdNET using the BirdNET Analyzer. The number in brackets shows how much faster than real-time this represents:
Model | Core i5 CPU | Core i5 GPU | M2 CPU | M2 WebGPU |
---|---|---|---|---|
Chirpity native | 80s (140x) | 28s (388x) | 67s (167x) | 44s (252x) |
Chirpity BirdNET | 106s (106x) | 270s (41x) | 75s (148x) | N/A |
BirdNET Analyzer | 549s (20x) | N/A | 126s (88x) | N/A |
An introductary tour of the key features of Chirpity.