On a long-haul flight, there's nothing worse than being sat next to a chatty stranger. But songbirds don't seem to mind, as a new study suggests they are likely to'talk' to other species as they migrate. Last year, a team of scientists discovered that birds seem to'buddy up' with other species at stopover sites during migration, but there was no evidence that different species pair up or communicate vocally on the wing. But now it's been found that the birds may even chat to gather important information about the journey they are on. For their new study the researchers, from the University of Illinois, analysed more than 18,000 hours of recorded flight calls made over three years in eastern North America.

Bioacoustic sensors, sometimes known as autonomous recording units (ARUs), can record sounds of wildlife over long periods of time in scalable and minimally invasive ways. Deriving per-species abundance estimates from these sensors requires detection, classification, and quantification of animal vocalizations as individual acoustic events. Yet, variability in ambient noise, both over time and across sensors, hinders the reliability of current automated systems for sound event detection (SED), such as convolutional neural networks (CNN) in the time-frequency domain. In this article, we develop, benchmark, and combine several machine listening techniques to improve the generalizability of SED models across heterogeneous acoustic environments. As a case study, we consider the problem of detecting avian flight calls from a ten-hour recording of nocturnal bird migration, recorded by a network of six ARUs in the presence of heterogeneous background noise. Starting from a CNN yielding state-of-the-art accuracy on this task, we introduce two noise adaptation techniques, respectively integrating short-term (60-millisecond) and long-term (30-minute) context. First, we apply per-channel energy normalization (PCEN) in the time-frequency domain, which applies short-term automatic gain control to every subband in the mel-frequency spectrogram. Secondly, we replace the last dense layer in the network by a context-adaptive neural network (CA-NN) layer, i.e. an affine layer whose weights are dynamically adapted at prediction time by an auxiliary network taking long-term summary statistics of spectrotemporal features as input. We show that both techniques are helpful and complementary. [...] We release a pre-trained version of our best performing system under the name of BirdVoxDetect, a ready-to-use detector of avian flight calls in field recordings.

CLO-WTSP: 16,703 labeled audio clips captured by remote acoustic sensors deployed in Ithaca, NY and NYC over the fall 2014 and spring 2015 migration seasons. Each clip is labeled to indicate whether it contains a flight call from the target species White-Throated Sparrow (WTSP), a flight call from a non-target species, or no flight call at all. CLO-SWTH: 179,111 labeled audio clips captured by remote acoustic sensors deployed in Ithaca, NY and NYC over the fall 2014 and spring 2015 migration seasons. Each clip is labeled to indicate whether it contains a flight call from the target species Swainson's Thrush (SWTH), a flight call from a non-target species, or no flight call at all. CLO-WTSP: 16,703 labeled audio clips captured by remote acoustic sensors deployed in Ithaca, NY and NYC over the fall 2014 and spring 2015 migration seasons.