轻量化多层感知网络的海洋声纹识别OA

To enhance the classification efficiency and noise resistance capability of marine acoustic fingerprint signals while reducing the computational cost of traditional deep learning models for resource-constrained underwater devices,a lightweight method for marine acoustic recognition is investigated in this paper.By integrating Wiener filtering with a lightweight multilayer perceptron network,an end-to-end recognition pipeline is constructed.The process begins with extraction of Mel-frequency cepstral coefficient features from the acoustic data.An adaptive noise assessment mechanism is then employed to filter and denoise samples with high noise levels,while a lightweight classification network is utilized to achieve high-performance signal recognition.Experiments are demonstrated with a diverse dataset comprising real marine bio-acoustic data from multiple species.The results demonstrate that the proposed method achieves stable overall classification accuracy and exhibits strong recognition performance across different species and their varying behavioral vocalizations.Furthermore,the approach significantly reduces signal processing time and delivers highly robust recognition capabilities on low-computational-power devices.This provides a feasible technical solution for edge monitoring equipment operating in complex marine environments.