Automatic Bird Sound Detection

Avian bioacoustics research was greatly assisted by the introduction of autonomous recording units, which not only allow remote monitoring but also make large-scale studies possible. However, manual inspection of acoustic recordings becomes more challenging with increasingly larger datasets. In this study, we developed a logistic model to predict the probability of bird presence in audio recordings using sound frequency percentiles. The acoustic recordings covered bird songs and calls in a wide range of environments (e.g., grassland, forest, urban areas) along with the presence of noise due to weather, traffic, insects, and human speech.

In this study, we developed a logistic model to predict the probability of bird presence in audio recordings using sound frequency percentiles. The acoustic recordings covered bird songs and calls in a wide range of environments (e.g., grassland, forest, urban areas) along with the presence of noise due to weather, traffic, insects, and human speech.