Multi-label Bird Species Classification Using Transfer Learning Network

Bird sounds collected in the field usually include multiple birds of different species vocalizing at the same time, and the overlapping bird sounds pose challenges for species recognition. Extracting effective acoustic features is critical to multi-label bird species classification task. This work has extended an efficient transfer learning technique for labelling and classifying multiple bird species from audio recordings, further laying the foundation for conservation plans. A synthetic dataset was created by randomly mixing original single-species bird audio recordings from the Cornell Macaulay Library. The final dataset consists of 28 000 audio clips, each 5 s long, containing overlapping vocalizations of two or three bird species among 11 different species. Several pre-trained convolutional neural networks (CNNs), including InceptionV3, ResNet50, VGG16, and VGG19, were evaluated for extracting deep features from audio signals represented as mel spectrograms. The long short-term memory network (LSTM) was further employed to extract temporal features. A multi-label bird species classification was investigated. The absolute matching rate, accuracy, recall, precision, and F1-score of the InceptionV3+LSTM model for multi-label bird species classification are 98.25 %, 99.32 %, 99.41 %, 99.90 %, and 99.57 %, respectively, with the minimum Hamming loss of 0.0062. The results show that the proposed method has excellent performance and can be used for multi-label bird species classification.