# Reconstruction Modeling and Validation of Brown Croaker (Miichthys miiuy) Vocalizations Using Wavelet-Based Inversion and Deep Learning

## TL;DR

Researchers developed a method to accurately reconstruct the vocalizations of brown croaker using wavelet synthesis and deep learning, enabling better acoustic monitoring.

## Contribution

A novel framework combining wavelet-based inversion and deep learning for high-fidelity reconstruction of fish vocalizations.

## Key findings

- Wavelet-based synthesis with PSO optimization achieved >98% waveform similarity in reconstructed fish vocalizations.
- A ResNet-18 Siamese network validated near-unity similarity (~0.9996) between real and reconstructed signals.
- The method preserves SPL values and IPI distributions, even under varying SNR conditions.

## Abstract

We developed a wavelet-based framework using fk14 synthesis and particle swarm optimization (PSO) to reconstruct brown croaker vocalizations.

Parameter sensitivity analysis identified delay and scale as key factors, enabling >98% waveform similarity in marine bioacoustic reconstruction.

A ResNet-18-based Siamese network validated near-unity similarity (~0.9996) and evaluated across varying SNR conditions.

This method enables high-fidelity bioacoustic signal synthesis for passive monitoring and machine learning data augmentation.

Fish species’ biological vocalizations serve as essential acoustic signatures for passive acoustic monitoring (PAM) and ecological assessments. However, limited availability of high-quality acoustic recordings, particularly for region-specific species like the brown croaker (Miichthys miiuy), hampers data-driven bioacoustic methodology development. In this study, we present a framework for reconstructing brown croaker vocalizations by integrating fk14 wavelet synthesis, PSO-based parameter optimization (with an objective combining correlation and normalized MSE), and deep learning-based validation. Sensitivity analysis using a normalized Bartlett processor identified delay and scale (length) as the most critical parameters, defining valid ranges that maintained waveform similarity above 98%. The reconstructed signals matched measured calls in both time and frequency domains, replicating single-pulse morphology, inter-pulse interval (IPI) distributions, and energy spectral density. Validation with a ResNet-18-based Siamese network produced near-unity cosine similarity (~0.9996) between measured and reconstructed signals. Statistical analyses (95% confidence intervals; residual errors) confirmed faithful preservation of SPL values and minor, biologically plausible IPI variations. Under noisy conditions, similarity decreased as SNR dropped, indicating that environmental noise affects reconstruction fidelity. These results demonstrate that the proposed framework can reliably generate acoustically realistic and morphologically consistent fish vocalizations, even under data-limited scenarios. The methodology holds promise for dataset augmentation, PAM applications, and species-specific call simulation. Future work will extend this framework by using reconstructed signals to train generative models (e.g., GANs, WaveNet), enabling scalable synthesis and supporting real-time adaptive modeling in field monitoring.