Bioacoustic signal classification in continuous recordings: Syllable-segmentation vs sliding-window

Abstract

Frog population has been experiencing rapid decreases worldwide, which is regarded as one of the most critical threats to the global biodiversity. Therefore, large volumes of frog recordings have been collected for assessing this decline. Building an automatic frog species classification system is becoming ever more important. The traditional system for classifying frog species consists of four steps: (1) bioacoustic signal preprocessing, (2) segmentation, (3) feature extraction, (4) classification. Each prior step has a direct impact on the subsequent step. Consequently, the final classification performance is highly affected by the initial three steps. However, the performance of bioacoustic signal segmentation is highly dependent on the background noise of those environmental recordings. In this study, we propose an end-to-end approach for acoustic classification of frog species in continuous recordings. First, a sliding window is used to segment the audio signal into frames. Then, 1D-Convolution Neural Network and long short-term memory (CNN-LSTM) network is used to learn a representation from the raw audio signal, where three Convolutional layers and one LSTM layer are used to capture the signal’s pattern. Experimental results in classifying 23 Australian frog species demonstrate the effectiveness of our proposed CNN-LSTM based method. Compared to the syllable-segmentation based frog species classification system, our proposed CNN-LSTM based approach is more robust in frog species classification under various noisy conditions.