Advanced Detection of Fishing Vessels Using Yolov5: Innovations in Maritime Biotechnology

Abstract

This study addresses the inefficiencies of traditional music style classification methods, which are typically hampered by high-dimensional feature matrices leading to significant space costs and suboptimal accuracy. We propose a refined approach leveraging neural network technology, specifically utilizing Mel Frequency Cepstral Coefficients (MFCC) for feature extraction from music samples. Our method enhances the precision of classification through an optimized two-step process involving weighting and windowing of the music data, followed by the application of Recurrent Neural Networks (RNN) for style classification. Empirical simulations demonstrate that our method not only boosts classification accuracy by at least 16.36% compared to conventional techniques but also significantly reduces the space and time costs associated with the process, thus enhancing its viability for practical applications. This research contributes to the growing field of intelligent music analysis and presents a scalable solution for commercial technologies in the music industry.