Improved spectral dynamic features extracted from audio data for classification of marine vessels

Abstract

Underwater sound classification presents a unique challenge due to the complex propagation characteristics of sound in water, including absorption, scattering, and refraction. These complexities can distort and alter spectral features, hindering the effectiveness of traditional feature extraction methods for vessel classification. To address this challenge, this study proposes a novel feature extraction method that combines Mel-frequency cepstral coefficients (MFCCs) with a spectral dynamic feature (SDF) vector. MFCCs capture the spectral content of the audio signal, whereas SDF provides information on the temporal dynamics of spectral features. This combined approach aims to achieve a more comprehensive representation of underwater vessel sounds, potentially leading to improved classification accuracy. Validation with real-world underwater audio recordings demonstrated the effectiveness of the proposed method. Results indicated an improvement of up to 94.68% in classification accuracy when combining SDF with several classical extractors evaluated. This finding highlights the potential of SDF in overcoming the challenges associated with underwater sound classification.