Bird acoustic activity detection

Audio event recognition methods based on the Hidden Markov Model / Gaussian Mixture Model (HMM/GMM) often depend on a large number of mixture components or multi-stage models that require significant computational and memory resources during their operation. A widely used approach for coping with complexity is employing an acoustic activity detector, which selects for further processing only those portions of the audio that are considered promising. As a result, the audio feature extraction and the subsequent pattern recognition stages will process only a subset of the original audio stream, helping to reduce the misclassification rates while lowering the computational demands. In the present work we propose a method for bird acoustic activity detection, based on morphological filtering of the spectrogram seen as an image. The practical significance of the proposed method is validated on the automated acoustic recognition of Southern Lapwing Vanellus chilensis, a common Neotropical bird species. Compared with other methods of acoustic activity detection it demonstrates advantageous performance.