Whales communicate using whistle vocalizations that are essentially underwater acoustic frequency-modulated tones. Inevitable environmental noise decreases recognition accuracy of these sounds during wide range detection. In this paper, we propose a robust time − frequency analysis method that combines resonance sparse signal decomposition (RSSD) and spectrogram ridge extraction. We apply RSSD to extract whistle components from the raw signal, and then we segment the ridge regions of the whistle spectrograms. By applying a partial derivative method, we extract the whistle spectrogram ridge representing an accurate trace of the whistle vocalization. From these results, we extract ridge features and use an SVM or a random forest to identify the whale species. We evaluated our method using experiments with samples for four whale species. Compared with direct ridge extraction directly without RSSD, our proposed method achieved better extraction of frequency characteristics of the vocalizations. Our proposed method achieved an accuracy rate of over 98% for sounds from four species when using five training samples. INDEX TERMS Classification of whale vocalization, resonance sparse signal decomposition, tunable Qfactor wavelet transform (TQWT), morphological component analysis (MCA), ridge extraction.

Whale sounds may mix several elements including whistle, click, and creak in the same vocalization, which may overlap in time and frequency, so it leads to conventional signal separation techniques challenging to be applied for the signal extraction. Unlike conventional signal separation techniques which are based on the frequency bands, such as WT and EMD, tunable-Q wavelet transform (TQWT) can separate the objected signal into particular components with different structures according to its oscillation property and eliminate in-band noise using the basis pursuit method. Considering the characteristics of oscillatory and transient impulse, we propose a novel signal separation method for whale whistle and click extraction. The proposed method is performed by the following two steps: first, TQWT is used to construct the dictionary for sparse representation. Secondly, the whale click and whistle construction are performed by designing the basis pursuit denoising (BPD) algorithm. The proposed method has been compared with one of the popular signal decomposition techniques, i.e., the EMD method. The experimental results show that the proposed method has a better performance of click and whistle signal separation in comparison with the EMD algorithm.