Acoustic bird species classification under low SNR and small-scale dataset conditions

Abstract

Bird species classification based on acoustic monitoring has attracted increasing attention. Two common methods, deep learning and feature extraction-based, are widely applied for bird sound classification. Although deep learning based methods have shown great success, they usually require an enormous amount of labeled data to train models, which could make these approaches prohibitive since collecting enough labeled samples of various species, especially rare birds, is a difficult task. On the other hand, feature extraction based methods are less dependent on the number of samples and have achieved remarkable classification performance for small-scale datasets, however they prove susceptible to signal-to-noise ratio (SNR). To address this problem, a new method is proposed for acoustic bird species classification in this work, which is well suited to low SNR and small-scale dataset conditions. Firstly, an adaptive threshold scheme based on the constant false alarm rate (CFAR) criterion is designed to more effectively detect potential bird sound segments in field recordings. Then, the local wavelet acoustic pattern (LWAP) and Mel-frequency cepstral coefficients (MFCC) features are extracted, which are further processed using improved vector of locally aggregated descriptors (VLAD) encoding. Finally, encoded LWAP and MFCC features are fused with dimensionality reduction and a classifier with weighted distance is used for classification. Corresponding feature space analysis upon feature fusion is also provided demonstrating explicit improvement in bird sounds discrimination under low SNR conditions. Experimental results on the dataset consisting of 11 bird species under low SNR and small-scale conditions reveal that the proposed method achieves considerable improvement in classification performance as compared with other feature extraction based methods. Meanwhile, our approach also outperforms state-of-the-art deep learning based methods.