Automatic Segmentation of Audio Signals for Bird Species Identification

The identification of bird species from their audio recorded songs are nowadays used in several important applications, such as to monitor the quality of the environment and to prevent bird-plane collisions near airports. The complete identification cycle involves the use of: (a) recording devices to acquire the songs, (b) audio processing techniques to remove the noise and to select the most representative elements of the signal, (c) feature extraction procedures to obtain relevant characteristics, and (d) decision procedures to make the identification. The decision procedures can be obtained by Machine Learning (ML) algorithms, considering the problem in a standard classification scenario. One key element is this cycle is the selection of the most relevant segments of the audio for identification purposes. In this paper we show that the use of short audio segments with high amplitude - called pulses in our work - outperforms the use of the complete audio records in the species identification task. We also show how these pulses can be automatically obtained, based on measurements performed directly on the audio signal. The employed classifiers are trained using a previously labeled database of bird songs. We use a database that contains bird song recordings from 75 species which appear in the Southern Atlantic Coast of South America. Obtained results show that the use of automatically obtained pulses and a SVM classifier produce the best results; all the necessary procedures can be installed in a dedicated hardware, allowing the construction of a specific bird identification device.