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Bird phrase segmentation by entropy-driven change point detection

A bird phrase segmentation method using entropy-based change point detection is proposed. Spectrograms of bird calls are usually sparse while the background noise is relatively white. Therefore, considering the entropy of a sliding time-frequency block on the spectrogram, the entropy dips when detecting a signal and rises when the signal ends. Rather than applying a hard threshold on the entropy to determine the beginning and ending of a signal, a Bayesian change point detection is used to detect the statistical changes in the entropy sequence. Tests on a database of Cassin's Vireo (Vireo cassinii), our proposed segmentation method with spectral subtraction or a novel spectral whitening method as the front-end generates more accurate time labels, lower the false alarm rate than the conventional time-domain energy detection method and achieves high phrase classification rate.