Optimization and imputation of acoustic absorption sequence prediction for transversely isotropic porous materials: A MultiScaleCNN-Transformer-PSO model approach

Abstract

To address the limitations of traditional methods in real-time prediction and adaptive optimization of sound absorption coefficients for transversely isotropic porous materials. This study introduces a method combining a Multi-Scale Convolutional Neural Network (MultiScaleCNN) and a Transformer model to improve real-time prediction and optimization of sound absorption coefficients in transversely isotropic porous materials. Using a validated transfer matrix method based on the Biot model and acoustic analysis via COMSOL 6.2, a database of sound absorption curves was created. MultiScaleCNN extracts multi-scale features, which the Transformer model uses for sequence-to-sequence modeling with a self-attention mechanism, effectively managing complex dependencies. The model achieves high accuracy in full-mask prediction, closely aligning with actual values. Additionally, Particle Swarm Optimization (PSO) optimizes sound absorption coefficients in the 1000–2000 Hz range, meeting the fitness function’s criteria. For partial-mask imputation, the model uses adjacent frequency data and features to enhance accuracy. Results show that the MultiScaleCNN-Transformer-PSO model offers a robust, data-driven approach for predicting and optimizing sound absorption coefficients, advancing the field.