This review provides a comprehensive overview of modern experimental and numerical methods for characterizing the sound absorbing properties of dissipative sound-absorbing materials. Experimentally, we summarize both in situ techniques (e.g., pulse reflection, two-microphone, p-u probe, and spatial Fourier transform method) and laboratory methods (e.g., impedance tube, transfer function, and reverberation room methods), discussing their principles and applications. For the numerical methods, we detail the development and refinement of empirical models (e.g., Delany–Bazley, Miki, Komatsu), theoretical models (e.g., Johnson–Champoux–Allard), and computer numerical methods, along with methods for obtaining flow resistivity, including empirical formulas, experimental measurements. Furthermore, we review recent advances in machine learning approaches (e.g., generalized regression neural networks, radial basis function neural networks, and artificial neural networks) for predicting the sound absorption coefficient. This work aims to serve as a methodological reference for the research, development, and performance evaluation of dissipative sound-absorbing materials.

As engines trend toward miniaturization, lightweight design, and higher power density, noise issues have become increasingly prominent, necessitating precise radiated noise prediction for effective noise control. This study develops a machine learning model based on surface vibration test data, which enhances the efficiency of engine noise prediction and has the potential to serve as an alternative to traditional high-cost engine noise test methods. Experiments were conducted on a four-cylinder, four-stroke diesel engine, collecting surface vibration and radiated noise data under full-load conditions (1600–3000 r/min). Five prediction models were developed using support vector regression (SVR, including linear, polynomial, and radial basis function kernels), random forest regression, and multilayer perceptron, suitable for non-anechoic environments. The models were trained on time-domain and frequency-domain vibration data, with performance evaluated using the maximum absolute error, mean absolute error, and median absolute error. The results show that polynomial kernel SVR performs best in time domain modelling, with an average relative error of 0.10 and a prediction accuracy of up to 90%, which is 16% higher than that of MLP; the model does not require Fourier transform and principal component analysis, and the computational overhead is low, but it needs to collect data from multiple measurement points. The linear kernel SVR works best in frequency domain modelling, with an average relative error of 0.18 and a prediction accuracy of about 82%, which is suitable for single-point measurement scenarios with moderate accuracy requirements. Analysis of measurement points indicates optimal performance using data from the engine top between cylinders 3 and 4. This approach reduces reliance on costly anechoic facilities, providing practical value for noise control and design optimization.