Multi-label bird species classification from audio recordings using attention framework

Abstract

For the conservation of avian biodiversity, bird detection is vital since it allows ornithologists to quantify which species exist in a particular area. Analyzing their acoustic signals enables the efficient identification of multiple bird species from overlapping recordings. This paper addresses classifying bird vocalizations in real-time audio recording using acoustic analysis. Schemes based on recurrent neural networks (RNN) are presented in the proposed work. Gated-recurrent units (GRU) are a particular type of RNN that has shown remarkable performance in acoustic classification. We propose a hierarchical Attention-based bidirectional gated recurrent unit (BiGRU) model for classifying acoustic signals of birds by using Mel-frequency cepstral coefficients (MFCC). The attention mechanism has proved its superior efficacy in many acoustic, speech and music processing applications. The attention mechanism is employed to give a different focus to the information outputted from the hidden layers of BiGRU. We adopted a short-time sliding-aggregation approach to decide on the test data, in which probability outcomes are species-wise summed and normalized. Species with the highest probability scores are assumed to be the dominant species in the recording. Our Attention-BiGRU classifier achieves pretty high performance in the Xeno-Canto dataset, with an F1-score of 0.84, with competing performance to the state-of-the-art multi-label classifiers.