Robust Endpoint Detection of Marine Biological Acoustic Signals in Underwater Noise Using Wavelet-Domain Adaptive Composite Thresholding

Endpoint detection is a key preprocessing step for passive acoustic monitoring of marine biological sounds, but underwater noise can obscure onset and offset boundaries, especially at low signal-to-noise ratios (SNRs). This study proposes a wavelet-domain adaptive composite thresholding (WD-ACT) method for robust endpoint detection. The method applies Daubechies 4 (Db4) wavelet enhancement with soft-thresholding, then fuses short-time energy, non-zero-lag autocorrelation, and spectral variation into a frame-level composite detection score. An adaptive dual-threshold strategy estimated from background-frame statistics is used to determine the onset and offset of effective acoustic segments. Experiments were conducted on three manually annotated marine mammal vocalization segments, including seal, dolphin, and walrus calls, mixed with white noise, pink noise, ship noise, and wind–wave noise at SNRs from −5 to 15 dB. For each combination of acoustic segment, noise type, and SNR level, 100 noise-mixing trials were performed. Compared with short-time energy and zero-crossing rate (STE-ZCR), time-domain autocorrelation detection (TDA), wavelet-domain detection (WD), and spectral-flux detection (SFD), WD-ACT achieved the lowest median missed detection rate (5.56%), the lowest median boundary mean absolute error (222.49 ms), and the highest median frame-level accuracy (87.17%). An additional generalization evaluation using nine independent vocalization segments from five further marine mammal categories provided additional evidence of cross-signal applicability without species-specific parameter tuning. These results indicate that WD-ACT provides a robust and interpretable preprocessing method for marine bioacoustic endpoint detection.