We've just released Birdvox-full-night, a new challenging dataset for machine learning on bioacoustic data! Details about the dataset and the models we benchmarked are provided in our ICASSP 2018 paper:
Birdvox-Full-Night: A Dataset and Benchmark for Avian Flight Call Detection
V. Lostanlen, J. Salamon, A. Farnsworth, S. Kelling, and J. P. Bello
In IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), Calgary, Canada, Apr. 2018.
[PDF][Copyright]
This article addresses the automatic detection of vocal, nocturnally migrating birds from a network of acoustic sensors. Thus far, owing to the lack of annotated continuous recordings, existing methods had been benchmarked in a binary classification setting (presence vs. absence). Instead, with the aim of comparing them in event detection, we release BirdVox-full-night, a dataset of 62 hours of audio comprising 35402 flight calls of nocturnally migrating birds, as recorded from 6 sensors. We find a large performance gap between energy based detection functions and data-driven machine listening. The best model is a deep convolutional neural network trained with data augmentation. We correlate recall with the density of flight calls over time and frequency and identify the main causes of false alarm.
You can download the dataset here: https://wp.nyu.edu/birdvox/birdvox-full-night/
You can also check out additional bioacoustic datasets for machine learning we have released as part of the BirdVox project here: https://wp.nyu.edu/birdvox/codedata/#datasets
Finally, if you're at ICASSP 2018 and want to learn more be sure to grab my esteemed colleague Vincent Lostanlen for a chat!
Birdvox-Full-Night: A Dataset and Benchmark for Avian Flight Call Detection
V. Lostanlen, J. Salamon, A. Farnsworth, S. Kelling, and J. P. Bello
In IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), Calgary, Canada, Apr. 2018.
[PDF][Copyright]
This article addresses the automatic detection of vocal, nocturnally migrating birds from a network of acoustic sensors. Thus far, owing to the lack of annotated continuous recordings, existing methods had been benchmarked in a binary classification setting (presence vs. absence). Instead, with the aim of comparing them in event detection, we release BirdVox-full-night, a dataset of 62 hours of audio comprising 35402 flight calls of nocturnally migrating birds, as recorded from 6 sensors. We find a large performance gap between energy based detection functions and data-driven machine listening. The best model is a deep convolutional neural network trained with data augmentation. We correlate recall with the density of flight calls over time and frequency and identify the main causes of false alarm.
You can download the dataset here: https://wp.nyu.edu/birdvox/birdvox-full-night/
You can also check out additional bioacoustic datasets for machine learning we have released as part of the BirdVox project here: https://wp.nyu.edu/birdvox/codedata/#datasets
Finally, if you're at ICASSP 2018 and want to learn more be sure to grab my esteemed colleague Vincent Lostanlen for a chat!
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