Robust Dolphin Whistle Detection Based on Dually-Regularized Non-Negative Matrix Factorization in Passive Acoustic Monitoring

Underwater passive acoustic monitoring (PAM) serves as a core approach pervasively applied to the long-term, non-invasive detection of biological acoustic signals. Dolphin whistles serve as a fundamental aspect of vocal communication, exhibiting intricate frequency-modulated structures. Robust detection of these whistles is essential for dolphin species diversity conservation, yet performance is frequently compromised by underwater background noise, leading to significant degradation in detection reliability. To address this issue, this paper presents an unsupervised enhancement method based on Dually-Regularized Non-Negative Matrix Factorization (DR-NMF). Beyond a standard data fidelity term, the proposed framework integrates two specialized regularizers, including Overlapping Group Shrinkage and Group Lasso. The former promotes time–frequency continuity of whistle ridges, while the latter adaptively eliminates redundant bases, achieving an improved trade-off between structural integrity and noise suppression. The optimization procedure employed a combination of majorization–minimization, iteratively reweighted least squares, and proximal gradient techniques, all of which were implemented within an alternating minimization scheme featuring nested inner–outer iterations. This architecture ensures stable convergence and computational practicality. Extensive experimental evaluations under diverse low signal-to-noise ratio (SNR) conditions reveal that the proposed method achieves a substantial improvement in recall without compromising precision, resulting in consistent enhancements in frame-level F1-scores. When applied to real-world dolphin whistle recordings, our method outperforms existing baseline approaches, demonstrating remarkable robustness in detecting whistle signals when amidst challenging marine environmental noise.