D-IFGNet: D-pad based robust cardiac sound classification with IMF noise-aware gating and clean-adaptive feature fusion

Automated cardiac sound classification using phonocardiograms (PCGs) is pivotal for early cardiovascular disease screening, yet its reliability is frequently challenged by environmental noise and physiological interference such as lung sounds. To reconcile this robustness-fidelity trade-off, we propose D-IFGNet, a novel framework that integrates a D-PAD-based decomposition backbone with IMF Noise-Aware Gating and Clean-Adaptive Feature Fusion. Unlike methods relying on fixed basis functions, our architecture employs a learnable decomposition-reconstruction-decoupling mechanism to extract intrinsic mode-like components directly from raw signals. The IMF Noise-Aware Gating module utilizes frequency-band priors and learned corrections to dynamically reweight these components, effectively isolating diagnostic features from complex interference. Furthermore, the Clean-Adaptive Feature Fusion module incorporates a soft switching strategy to adaptively balance noise-robust aggregation with the preservation of local temporal details in high-quality recordings. Extensive experiments on datasets contaminated with Additive White Gaussian Noise (AWGN) and real-world lung sounds demonstrate that D-IFGNet achieves consistent robustness across heterogeneous acoustic environments. In noisy conditions, our method outperforms the state-of-the-art baseline NRC-Net, improving classification accuracy by an average of 1.55% while maintaining strong diagnostic precision on clean clinical signals.