Modeling Time-Varying Nonlinear Loudspeaker Displacement With Physics-Guided Dilated Convolutional Networks

Moving-coil loudspeakers exhibit strong large-signal nonlinearities and complex time-varying mechanical drift due to suspension viscoelasticity, which challenges accurate long-term displacement prediction. This paper proposes a physics-guided WaveNet (PG-WaveNet) that integrates physical priors as multichannel local conditioning. The conditioning signals are built from low-frequency voice-coil current energy and leaky-integral histories. Receptive field analysis shows that strongly time-varying behavior requires temporal context extending well below resonance, where purely data-driven WaveNet models become limited. Experiments on two loudspeakers with different degrees of viscoelasticity show that PG-WaveNet improves both accuracy and robustness over a standard WaveNet, a long short-term memory network, and a nonlinear state-space function model. On the strongly time-varying dataset, PG-WaveNet achieves 2.6 dB lower NMSE and 4.75 dB lower maximum absolute error than the standard WaveNet and maintains stable tracking over continuous 60-s intervals under multitone and music excitation. Ablation results confirm that medium-term and long-term conditioning channels are essential when mechanical drift exceeds the network’s receptive field.