Synthesis and modification of humpback whale song units based on hidden Markov model for bio-inspired applications

Humpback whales produce a wide variety of frequency-modulated vocalizations, called song units. Modeling and synthesis of these units form the basis for many bio-inspired applications, including underwater covert communication and naturalistic playback experiments. Conventional synthesis methods are based on fundamental frequency contour modeling of single-segment signals, which exhibit limitations in terms of synthesis flexibility and similarity. To address the above limitations, this paper proposes a humpback whale song unit synthesis method based on small-sample training. Fundamental frequency contours and line spectral pairs are extracted from humpback whale song units collected in marine environments to construct the training dataset. Using these parameters, a hidden Markov model (HMM) is established for parameter training, and probability density functions are obtained for each HMM state. To address high-frequency jitter in generated fundamental frequency contours, a parameter generation method that combines dynamic feature constraints with variational mode decomposition denoising is introduced, yielding smoother fundamental frequency curves. For enhanced synthesis flexibility, state duration modification and fundamental frequency modification methods are proposed based on parameter distributions. Finally, the generated parameters are converted into time-domain waveforms using a linear predictive coding-pitch vocoder. To comprehensively evaluate the synthesis performance, an assessment framework based on statistical parametric analysis and t-distributed stochastic neighbor embedding is established. Simulation results demonstrate that the proposed humpback whale song unit synthesis system achieves superior flexibility and similarity compared to the conventional approach based on single whistles modeling, ultimately enhancing performance in bio-inspired applications.