Noise-Adversarial Denoising of Motor Acoustic Signals Across Noise Domains.

Motor acoustic monitoring in industrial sites is often compromised by background noise, machine self-noise and shifts between noise domains, which distort the acoustic-signature structure. Here we propose a Noise-Adversarial Denoising Enhancement Network (NADEN) for motor acoustic-signal denoising under complex noise. NADEN maps noisy waveforms into the short-time Fourier transform magnitude domain and predicts a time-frequency mask with an encoder and mask decoder head. This design suppresses noise-dominated time-frequency units while retaining target acoustic structure. A multi-resolution STFT loss constrains spectral consistency across scales, and a gradient reversal layer supports noise-domain adversarial learning. Experiments on a MUSAN-based multi-domain motor acoustic dataset showed that NADEN reduced mean spectral entropy from 0.6288 to 0.6098 while leaving spectral flatness largely unchanged. It also reduced the standard deviation of SFM from 0.0744 to 0.0688, indicating lower spectral randomness and more stable outputs. Sensitivity and ablation experiments support the complementary roles of multi-resolution spectral constraints and noise-domain adversarial learning. These findings suggest a feasible data-driven route for enhancing motor acoustic signals in complex industrial noise environments.