Detecting frog calling activity based on acoustic event detection and multi-label learning

Frog population has been declining the past decade for habitat loss, invasive species, climate change, and so forth. Therefore, it is becoming ever more important to monitor the frog population. Recent advances in acoustic sensors make it possible to collect frog vocalizations over large spatio-temporal scale. Through the detection of frog calling activity with collected acoustic data, frog population can be predicted. In this paper we propose a novel method for detecting frog calling activity using acoustic event detection and multi-label learning. Here, frog calling activity consists of frog abundance and frog species richness, which denotes number of individual frog calls and number of frog species respectively. To be specific, each segmented recording is first transformed to a spectrogram. Then, acoustic event detection is used to calculate frog abundance. Meanwhile, those recordings without frog calls are filtered out. For frog species richness, three acoustic features, linear predictive coefficients, Mel-frequency Cepstral coefficients and wavelet-based features are calculated. Then, multi-label learning is used to predict frog species richness. Lastly, statistical analysis is used to reflect the relationship between frog calling activity (frog abundance and frog species richness) and weather variables. Experiment results show that our proposed method can accurately detect frog calling activity and reflect its relationship with weather variables