Optimized aquaculture feeding through matched-filter audio signal processing and machine learning

Accurate feed management in marine aquaculture is critical for maximizing fish growth and minimizing environmental impacts. While previous approaches have leveraged acoustic monitoring and neural network-based classification to assess feeding, these methods often lack scalability and precision for industrial deployment. This study introduces a matched-filter audio signal processing technique, informed by domain knowledge, to continuously quantify fish feeding intensity in gilthead seabream (Sparus aurata) aquaculture. By extracting a species-specific bite acoustic template and applying matched filtering with sliding window aggregation, we generate a robust, continuous feeding intensity label from passive acoustic recordings. Machine learning regression models (XGBoost and Random Forest) validate the approach, demonstrating that environmental and biological variables explain 89% of the variation in feeding intensity. The proposed methodology offers a simple, scalable, and cost-effective solution for real-time feed optimization and welfare monitoring in aquaculture systems. By reducing data dimensionality and enhancing sensitivity to subtle behavioral changes, this framework supports the deployment of advanced data-driven monitoring tools and paves the way for practical integration in commercial aquaculture operations. • Detection and quantification of fish feeding intensity are achieved through passive acoustic signals analyzed with a matched filter applied within a sliding-window framework. • A domain-aware data reduction scheme is employed, transforming high-frequency raw signals into behaviorally meaningful measures that reduce computational demands while preserving information relevant to feeding activity. • Regression-based machine learning models explained up to 89% of the variance in feeding intensity using environmental and biological variables. • The method was designed as a simple and scalable solution to support automated monitoring in aquaculture systems.